GenOL: Generating Diverse Examples for Name-only Online Learning
Authors: Minhyuk Seo
Seongwon Cho
Minjae Lee
Diganta Misra
Hyeonbeom Choi
Seon Joo Kim
Jonghyun Choi
Venue: Accepted by TMLR ()

Abstract: Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuously arriving online data streams incorporate new concepts (e.g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, `name-only' setup has been proposed, requiring only the name of concepts, not the supervised samples. A recent approach tackles this setup by supplementing data with web-scraped images, but such data often suffers from issues of data imbalance, noise, and copyright. To overcome the limitations of both human supervision and webly supervision, we propose GenOL using generative models for name-only training. But naive application of generative models results in limited diversity of generated data. Here, we enhance (i) intra-diversity, the diversity of images generated by a single model, by proposing a diverse prompt generation method that generates diverse text prompts for text-to-image models, and (ii) inter-diversity, the diversity of images generated by multiple generative models, by introducing an ensemble strategy that selects minimally overlapping samples. We empirically validate that the proposed \frameworkname outperforms prior arts, even a model trained with fully supervised data by large margins, in various tasks, including image recognition and multi-modal visual reasoning.

Published in Transactions on Machine Learning Research (10/2025)
GenOL: Generating Diverse Examples for Name-only Online Learning
Minhyuk Seo*
KU Leuven, Seoul National University
Seongwon Cho
Seorl National University
Minjae Lee
Seoul National University
Diganta Misra
ELLIS MPI-1S Tübingen
Hyconbeom Choi
Seoul National University
Scon Joo Kim
Yonsei University
Jonghyun Choi!
Seorl National University
Reviewed on OpenReview: https: // openreview. net/ formm? id= (ddo=PPPooTM...
Abstract
Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuuusly arriving online data streams incorporate new concepts (e-g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, 'name-only' setup has been proposed, requiring only the name of concepts, not the supervised sam- ples. A recent approach tackles this setup by supplementing data with web-scraped im- ages, but such data often suffers from issues of data imbalance, noise, and copyright. To overcome the limitations of both hurman supervision and webly supervision, we propose Generative name only Online Learning (GenOL) using generative models for name-only training. But naïve application of generative models results in limited diversity of gen- erated data. Here, we euhance (i) intra-diversity, the diversity of images generated by single model, by proposing diverse prompt generation method that generates diverse text prompts for toxt-to-image models, and (ii) inter-diversity, the diversity of images gener- ated by multiple generative models, by introducing an ensemble strategy that selects mini- mally overlapping samples. We empirically validate that the proposed GenOL out prior arts, even a model trained with fully supervised data by large margins, in various tasks, including image recognition and multi-modal visual reasoning. Code is available at https://github. cwm/ hom//summpraabgenoo
1 Introduction
Online learning has achieved remarkable progress, demonstrating improved model adaptability contin- uously evolving data streams. However, most existing approaches (Wang al., 2022 Kim al.] 2024a) still rely large, meticulously curated datasets that demand extensive human supervision. For instance, constructing the 423.5k clean images in DomainNet (Neyshabur et al. 2020) required over 50, 000 hours of manual filtering to remove outliers. In standard learning, where all training data are provided at once, the time spent on data collection and preprocessing does not affect performance, as these steps are completed prior to model training. In contrast, online learning continuously introduces new data that may encompass novel 'concepts' (e.g., classes, adjectives, and verbs multi-modal tasks) under distribution shifts, a set- ting known as continual learning (CI.), requiring ongoing data preparation throughout the training process. Consequently, delays in preparing data for newly encountered concepts in ouline learning can directly hinder the model's real-time adaptation to new concepts (Koh et al. 2021 Caccia et al. 2022
To address the issue of human annotation in the online setup, particularly under distribution shifts, Prabhu et al. 2024) propose 'name-only continual learning' setup, which requires only the names of new concepts,
First author: minhyyuleee@kyoneei...kkk
Corresponding authors.

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Published in Transactions on Machine Learning Research (10/2025)
Manually Annotated
Web-Scraped
Generated (Ours)
Dog
Dog
Dog
Controllability
Dependent on Existing Data
Dependent on Existing Data
Highly Customizable
Acquisition
Labor-Intensive
Existing Uploaded Data
Existing Generative Models
.ost
Noise
Well-Filtered
Irrelevant Content
Optimized Generative Models
Storage
Storage-Heavy
X
No Explicit Storage Cost
No Explicit Storage Cost
Requirements
Usage X
Freely Usable
Copyright Concerns
X
Freely Usable
Restrictions
Figure 1: Comparison of mamually annotated data, web-scraped data, and generated data (ours) for name only continual learning. Generated data overcomes usage restrictions (i.e., whether images can be used for learning) and offers controllability, enabling the generation of images in diverse contexts (e.g.. background, color) as desired. Compared to web-scraped data, generated data contains fewer irrelevant samples (i.c., lcss noisc), and they are more cost-cffective than MA data, which requires manual human annolation. For more details on the terminology used in this figure, see Section A.30
not the manually annotated data. They propose to use web-scraped data relevant to the given concepts as an alternative to manual annotation. Although web-scrapped data are abundant and easily accessible (Sun et al. 2018), challenges arise from copyright concerns (Zhang et al. 2023a). as well as inherent noise (Neyshabur et al. 2020), which significantly hinders the performance of continual learner (Kim et al. 2021
To tackle these challenges of using web-scrapped data, we propose leveraging text-to-image (T2I) generative models for name-only setup. Specifically, we propose a generative name-only framework, Generative name only Online Learning (GenOL), which takes only concepts input and trains on im- ages generated from given concepts without requiring labeled data. It takes advantage of generative models, such as controllability (Nie el al. 2021 (i.e., generating desired data) and unlimited image generation (Liang Additionally, it significantly accelerates data collection, e.g., generating
ctt al. 2022) as shown in Figure DomainNet with SDXL et al., 2023) on 8 NVIDIA RTX 4090 GPUs takes only S0 hours, compared to 50,000 hours for manual annotation. Note that GenOL can be utilized both general online learning setup and online continual learning under distribution shifts that introduces new concepts over time
In the name-only CL scenario, we focus on cases where new concepts represent relative novelly, concepts that are new to specific systems or domains, rather than completely novel concepts to the world (Cossu et al.) 2022] Shaheen et al. 2022). For example, deployed home assistant robot may need to learn new behaviors (Kim et al. 2024a like 'ride bike' and 'hold a bike', or e-commerce rocommendation systems may need to incorporale new calegories, such as bbladeesss fans' or 'electric scooters', after deployment. While these are common concepts, not all common concepts can be included in pre-deployment training due to the impracticality of anticipating all possible operational scenarios. To this end, we aim to train models with new concepts that are actually needed in their operational environment (i.e., personalization), leveraging generative models' broad coverage of most everyday concepts Ghaasem et al. 2024). We leave name-only CL for completely novel concepts unknown to generative models as future work.
Despite the advantages of generative models, generated images often suffer from limited diversity (Tian et al., [2244a] Bianchi et al., 2023 Fraser et al.] 2023). To address the limited diversity, we first de- fine intra-diversity and diver-diversity, which refer the diversity within data generated by single T21 model and the diversity among data generated by multiple T2I models, respectively. To improve intra- diversity, propose Hlerarchical Recurrent Prompt Generation (HIRPG), prompt generation method leveraging LLMss' in-context learning to create diverse text prompts. To enhance inter-diversity, we propose COmplexity-N ShnplexiyyNAviigaiing Avigating eNsembler (CONAN) a complexily-guidke data ensemble approach that aggre- gates samples from multiple generative models, selects a representative coreset, and trains a model exclusively on this coresct for efficient training and real-time adaptation to new concepts. We empirically demonstrate that GenOL significantly outperforms baselines across name-only dass-incremental learning, multimodal visual concept-ineremental learning setups, and even online learning setups without distribution shifts.
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In summary, address the following research questions, highlighting core contributions:
RQ1. Can generated data replace marual annotation in online learning setups? GenOL improves AAUC on the PACS OOD domain by 9% and 13% over models trained on web-scraped and MA data, respectively.
RQ2. How can we improve diversity in images generated by generative models? We propose HIRPG, a prompt generation method that leverages LLMs to create diverse text prompts for a given concept, which are then used by T2T generative models to generate varied images.
RQ3. How can we improve image diversity generated by multiple generators in an ensemble? We propose CONAN, a data ensembling strategy that considers the overlap and complexity of generated data.
2 Related Work
Learning from generative models. With the rise of robust generative models, several recent studies have leveraged synthetic data for training (Zhang et al.] 2024d] Tian et al. 2024b). To train a model with data generated by T21 generative models for given concept c, concept-specifie prompts Pe are required. Ramesh
ct al. )022); Jones et al. (2024) use the template "A photo of a c', in CLIP (Radford et al. 2021 to
construct prompt for concept e, while Sariyildız et al. (2023 claims that using just the concepl a prompt (p== "cc) yields better image generation. To add more concept-specific context to Pe, (Saryylddzz et al. (0023)
combine the concept name e with its definition de from WordNet (Miller 1995), resulting in Pe = "c, de
Nonetheless, all these approaches rely on a single type of Pe per concept, limiting the diversity of generated images despite the ability of T2T models to generate an unlimited number of images (Tian et al. 2024a )
To enhance the diversity of generated images, several prompt diversification methods have been proposed. LE (He et al. 2023) uses pre-trained word-to-sentence T5 model (Raffel et al. 2020 to generate diverse sentences that incorporate the given concepts. [arryildız et al. (2023) integrates the concept's hypernym he from WordNet and background scenes 6 from Places365 LLópez-Cifuena
et al. 2020), resulting in Pe = "c, he inside 6". In contrast to random background selection, Tian et al. (2024a) uses LLMs to generate contextually appropriate backgrounds for the given concept c, creating more plausible prompts. Similarly, Hammoud et al. (2024) and our proposod diverse prompt generation method, HIRPG, also employ an LLM for prompt generation. However, unlike previous LLM-based prompt generation methods, which do not consider the relationship between generated prompts, HIRPG minimizes overlap between them by providing previously generated prompts as negative examples to the LLM.
Coreset selection. Building a coreset from a larger candidate set and using only the coreset for training has been explored to reduce computational costs (Killamsetty et al. 2021c Xia et al.] 2024). Formally, from the candidate set T,, these methods select a coreset V ([V) < [T]), that preserves as much information from T as possible (Shin et al., 2023 To estimate the informativeness of the candidates, several metrics have been proposed, such as gradient (Pull et al. 2021 Pooladzandi et al. 2022 uncertainty (Coleman et al., 2020), influential score (Yang et al. 2022a Pooladzandi et al. 2022), and distance (Xia et al. 2023).
Although these methods effectively select coresets, many have substantial computational costs. Gradient- based methods (Paul et al. 2021 Pooladzandi et al. 2022: Shin et al. 2023). , which aim to minimize the difference between the gradients of the full training dataset T and the selected set V, require a well-trained model on T, which significantly increases computational overhead. Similarly, the influence based method (Yang et al.) (2022a) also requires significant computation due to the necessity of calculating the Hessian in the influence function, along with the iterative data selection process (Xia et al.) 2023). Tn contrast, distance-basel methods, such as Moderate (Xia et al. 2023) and our proposed CONAN, leverage well-trained feature extractor without backward processes, i.e., requiring only model forward passes for feature extraction, leading to faster data preparation time.
We review more relevant literature and provide extended related work in Sec. A.15 for space's sake.
3 Problem Statement of Name Only CL
Name-only CL setup (Prabhu et al. 2024) assumes that only new concepts y {11 B2.... are provided in stream, while prevalent online CL. setups assume well-curated annotated data (X,)) are given. The
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objective is to train model fo, parameterized by 0, to effectively understand the concepts up to the time step 1, i.e., {ue)i=ı- To train fo on given concepts, the learner can access public data such as web- scraped or generated data, To evaluate whether fe has learned concepts {vi at time step t, curated test data {A, Mi are used, where Xg refers to the set of data corresponding to the concept ys-
4 Approach
We propose the Generative name only Online Learning (GenOL) framework to address the absence of data in the name-only CL setup. The GenOL framework consists of four components: (i) Prompt Generation
Module 4 (Section 4.1
(ii) Text-to-Image Generators 9 (Section 4.2 (iii) Complexity- Ensembler A (Section 4.3) and (iv) a learner fe. We illustrate an overview of GenOL in Figure 2
When new concept is introduced, for which fo needs to be learned, generator 9 9 generates images related to the concept. Despite the capability of generative models to create an infinite number of images, the diversity of generated outputs is often limited (Liu et al.) 2023a; (aadtt et al. 2023). To improve the diver- sity, we introduce a diverse prompt generation module V, named Hlerarchical Recurrent Prompt Generation (HIRPG), which creates diverse text prompts and feeds them into T21 generative models. Furthermore, we enhance diversity using an ensembler A, which ensembles the images generated by multiple generators G while minimizing redundaney, thereby maximizing the diversity of the generated images. We name this Cehmplexity-AAvigatee
eNsembler (CONAN). Generated images are then streamed to the learner fo in real-time. Note that while it is possible to generate data for past concepts in real-time, we use episodic memory for efficiency, as it helps reduce computational costs.
ensemble strategy
4.1 Prompt Generation Module (1))
Prompt generation module & begins with a new concept c as input and constructs a base prompt PB using the template: 'A photo of c', following (Shtedritski et al. 2023 Shi et al. 2023). While PB can be used directly with the T2I generators G, generating multiple images using a single prompt may lead to limited diversity style, texture, and backgrounds (Fan et al., 2024 Therefore, to enhance intra-diversity, the diversity of images generated by a single generator, & leverages LLMs to generate additional diverse prompts.
A straightforward approach to generating N diverse prompts using LLMs is to generate N different prompts at once or to generate a single prompt N times, in previous work (He et al. 2023 Hammoud et al. 2024). However, generating N different prompts at once using LLM often generates duplicated prompts with similar semantics (Shur-Ofry et. al. 2024 Hayati et al., 2024). Similarly, in the case of generaling a single prompt N times, multiple inferences to an LLM with the same input can yield similar outputs (Zhang et al. 2024a Skapars et al. 2024), despite the non-deterministic nature of LLMs (Song et al. (024b).
Recurrent Prompt Generation (RPG). To reduce overlap between generated prompts, we iteratively generale new prompts that are distinct from those produced in previous steps. Inspired by previous work that solves complex problems by providing negative examples in in-context learning (Zhang et al., 2024b) and contrastive Chain-of-Though (Chia et al. 2023), we explicitly include previously generated prompts into the LLM input as negative examples. Presenting previously generated prompts and requesting new prompt distinct from them imposes hard constraint that effectively prevents overlap between newly generated prompt and the previous ones. Formally, this process is described as follows:
(PB) P. = -Ss; Ps), where S (PA
ii=
(1)
fi
where Ps denotes the system prompt and S; denotes the set of previously generated prompls up to the inh generation step. Since there are no negative examples in the initial step (f.e., i = =1), we use the base prompt PB = 'A photo of c' as the initial negative example. To generate N different prompts, we repeat the process N times. As previously generated prompts are iteratively used as negative examples, we call this process as Recurrent Prompt Generation (RPG). We provide the details of the system prompt Ps in the Section A.4
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where S441K denotes the set of previously generated nodes that share the same parent node Pa,-- By constructing a complete K-ary Tree with a depth of D, we can generate -K- including all internal and leaf nodes. This hierarchical generation allows for diverse prompt generation while Published in Transactions on Machine Learning Research (10/2025)
Concept C
G
Prompt Generation Module (t/)
Generators (g)
Ensembler (A)
C
Pai A dramatic stormy...
91
Pai stunning sunset...
Pax A serene mountain
N
Pa A photo of (c)
P
Pa twillggt urban
MXN
Pix A black and white
9M
Pax An aerial view of...
HIRPG
N
CONAN
Illustration of the proposed GenOL framework. When a new concept that. needs to be learned is encountered, it is passed through a prompt generation module, s to produce diverse prompts. These prompts are then used to generate data from a set of generators, G. The data generated by each generator are combined through the ensembler, A, and subsequently used to train the model, fo.
Figure 2:
However, generating a large number of prompts using RPG poses critical challenge. As the iterative steps repeated, the LLM imput length increases, causing difficulties in fully utilizing the long context, problem known as lost-in-the-middlle challenge (An el al. 2024 Liu et al. 2023c), well as substantial
computational overhead in con-con ICL (Li et al. 2024
Hlcrarchical Recurrent Prompt Generation (HIRPG). To address this challenge, we divide the RPG into multiple subtasks using hierarchical tree. Specifically, construct complete K-ary tree (Gross et al. 2018), where every internal node has K child nodes. Each node represents a generated prompt, with the root node (i.e., the node at depth d = 0) defined as PB = 'A photo of c². For fewer than K prompts, RPG is performed at depth d = 1. For more than K prompts, the tree extends to depth d 2 2, with each serving as the base prompt, as shown in Figure 2 Formally, focusing on the gth child node depth d, denoted as Pa,k (d N 0, 1 k S K), its child nodes Pati,K' (1 S k s K) are generated through the RPG as follows:
parent node at depth
(Pax) (Pax) U (att
if if 2,
P4+LN = (SS--IN;Ps), where S4+1K
(2)
k=+1_1 nodes (i.e., prompts),
bounding the number of negative examples by K (< N), thereby addressing both the lost-in-the-midde challenge and the computational overhead.
Since we divide RPG into subtasks using a hierarchical tree structure, we cannot consider nodes generated from different branches as negative examples. Nonethcless, overlap between generated prompts from different nodes is rare, as shown Section A.34 This because RPG in each node begins with distinct Pa,k Equation which serves as a negative example the first step (K' 1), and different examples in-context learning lead to different outputs qSu al.] 20022 Agarwal et al. 2024). We present an ablation study on the components of HIRPG to validate the contribution of each component, í.e., RPG and hierarchical generation (HTG), along with the pseudo code for vb in Section A.8 and Section A.17 respectively.
4.2 Text-to-lmage Generators (g)
To further amplify the diversity of generated images, employ multiple T2I generators G. Specifically, using a T21 generator 9:(-) E G and a prompt set P generated by v,, generate a set of images U, gi (P). At the end of generation using G, we obtain = !| Ug the union of images generated by G, with the same number of images generated by each model, i.c., U1] UUz] =.. [Ug/]] We provide detailed information
about the generators we employ in Section A.14
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4.3
Complexity-Guided Ensembler (A)
When ensembling images generated by different, T2T models, key question arises: Do we need to all of them? While training large-scale datasets is crucial for achieving performances Zhao et al.
2021 Yang
ct al. 2022b), it increases time-complexity and computational costs (Sharir et al. 2020 Kim et al. 2022
a8 well as carbon foolprint (Schwarlz et al. 2020 Pallerson el al. 2021). Moreover, in CL selups, extended training times hinder fast adaptation to new concepts (Seo et al. 2025).
Therefore, we select a coresct V from the entire generated data U, and train a learner only using Specifi- cally, to maintain the same training cost as single-generator setup while enhancing diversity, we sample the number of samples generated by single generator (i.e., [U/] samples) from U. A straightforward approach to constructing an ensemble set V is to sample equal number of images from each generator. However, surprisingly, this degrades performance, even compared to training on images from a single generator (i.e., no ensembling), as shown in Table 4 This degradation occurs because equal-weight sampling fails to account for the overlap between images, i.e., diversity.
Measuring Diversity using Relative Mahalanobis Distance (RMD) (Ren et al,, 2021 To en- hance diversity in the ensembled set V, we select samples positioned far from the concept prototype in the feature space, i.e., difficult samples, since these images are less likely to overlap with common images compared to those that are closer to the prototype (Zhang et al. 2024c Mehra et al.] 2025 Specifically, to select difficult samples in the ensemble set considering both concept-wiss wise difficulty and their relationship to other concepts, we employ the relative Mahalanobis distance (RMD) score (Ren al. 2021), which considers both the distance between sample and its concept prototype and the distance between the sample and the global prototype Cuu et al. 2023
RMD score for sample (xi, 3e) U is given follows:
RMD(II, y:) = M(Zi, Uii - -Mag
M(x,y) = DM (g(z),
Σ
f(z;)
U
M
JEU,
(3)
1
Magn(x)= DM g(z),
Σ f(xz))
U
JEU
where g(x) refers to the penultimate feature of the feature extractor g, DM refers to the Mahalanobis dis- tance (MD), U, denotes the set of samples belonging to class y, _M v1) and Maga (2;) represents class- wise MD and class-agnostic MD (i.e., global MD), respectively. If a sample is close to the class proto- type but far from the global prototype (i.e., low RMD score), it is easy to classify correctly. Conversely, if it is far from the class prototype but close to the global
Guitar
Giraffe
Elephant
Dog
Low RMD
RMD 0.43096
-4.59762
-1.21591
-16.2159
High RMD
protolype (i.e., high RMD score), the sample is hard to classify correctly and is more likely to be confused with nearby classes. show examples with low RMD scores and samples with high RMD scores in Figure 3
RMD 6.23886
6.32051
6.25181
6.1329
Figure 3: Samples with high vs. low RMD scores. Samples with low RMD scores have simple backgrounds or frontal views, while those with high scores show diverse backgrounds or vicwpoints.
Probabilistic Ensemble using RMD Score. A coreset, representative subset of an entire dataset (Anonymous) 2023),, includes not only sam- ples near the decision boundary but also concept-representative samples (Bang et al. 2021 Harun el al. 2023). Therefore, we adopt a probabilistic approach for ensemble selection, rather than simply choosing images with the highest RMD scores, to incorporate concept-represnntaiie samples. Specifically, we caleu- late Pule: the probability of selecting sample in concept c's coreset, detailed as follows. We first truncate samples with RMD scores in the upper and lower L% to eliminate outliers. Then, normalize the scores
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using Z-score normalization and apply softmax function to compute the selection probability as:
RMD.ael
Pule
(4)
RMD
W'U.
where Ug denotes the set of generated data for concept c, RMDule represents the normalized RMD score for sample Uc, and T denotes the temperature. This selection probability allows for sampling complex samples while also including a small portion of concept-repreeentaive samples in the ensemble set. We compare it with various RMD-based ensembles (e.g., top-k selection) in Section A.12
5 Experiments
5.1 Experimental Setup
Continual Learning Setups. We empirically validate GenOL by comparing it with state-of-the-art meth- ods in name-only clast-incremental learning (CIL) and name-only multi-modal visual-concepl incremental learning (MVCIL) setups (Seo al. 2025), where class names and concepts (e.g., 'ride bike', 'kick a ball') are encountered incrementally. MVCIL sctup requires both positive and negative support scts: positive sets contain images representing the concept, while negative sets include non-representalive images. Follow- ing Seo et al. 2205), we consider two tasks addressing following queries: (1) Concept answering (CA) What is the concept exclusively depicted by the positive support set? and (2) P/N- Given a query image, does it belong to the positive or negative support set?. Details of the MVCIL setup is in Section A.1
Models (fo). We ResNet-18 (He et al.) 2016) and ImageNet-1K pretrained ViT-base Dosovitskiy 2020) as a backbone network for the CIL experiments. For the MVCIL experiments, we use the LLa VA-1.5
7B Liu et al. 2023b), fine-tuning only the projection MLP layers and LoRA adapters (Hu et al.
2021 for
training efficiency, following Ye et al. (2023): Dong et al. (2024). In all experiments, we train a model with
ER (Rolnick et al.) 2019). which a simple but strong CL method (Prabhu et al. 2023 Sco et al. 2025).
Datasets. We evaluate GenOL 's domain generalization in CIL using PACS (Zhou et al. 2020 , CIFAR-
10-W (Sun et al.) 2024), ImageNet-R (Hendrycks et al. 2021), and DomainNet (Neyshabur et al. 2020
dividing them into multiple discrete tasks. Each DG benchmark consists of multiple domains, e.g., PACS contains Photo, Art, Cartoon, and Sketch domains. For comparison with the oracle scenario that assumes sufficient manually annotated (MA) data, we select rhoto-realistic domain as MA data for each benchmark.
This domain is treated as in-distribution (ID), while the remaining domains are considered out-of-distributie (OOD). For MVCIL experiments, we use Bongard-HOO (Jiang al. 2022 and Bongard-OpenWoot (Wu
ct al. 2024 We provide the details of the task split in Section A.2
Metrics. We report AxUC (Koh et al. 2021 Caccia et al. 2022 Koh et al. 2023 and Alast, which measure inference performance at any time and at the end of training, respectively. Note that for evaluation, we use the test set for seen concepts up to that point in time. We provide details on metrics in Section A.13
Basclines. We compare a model trained using GenOL with those using web-scraped data (C2C (Prabhu
et al. 2024 IE (Li el al. 2023b), Seafaring (Sato, 2023), and Tiara (Sato 2022 ). other synthetic dala
Glide (ynde-Syn(He et al. 2023 CHB (Saryyldız et al.J 2023) SC (Tian et al. 2024a), ) LE (He et al. 2023 and
CCG (Hammoud et al. 2024)) ), and MA data. Specifically, we compare HIRPG with diverse prompt gener-
ation baselines (CHB, SC, LE, and CCG). Furthermore, we integrate them with our proposed CONAN demonstrate CONAN's plug-and-play capability and ensure fair comparison with GenOI., which leverages multiple generators. Then, we compare CONAN with coreset selection baselines, i.e., Uncertainty (Coleman ct al. 2020), CRAIG MMrzasoleiimnn et al.] 2020), Glister (Killamsetty et al.) 2021b). GradMatch (Killam- setty et al.) 2021a Adacore (Pooladzandi et al. 2022), LCMat (Shin et al. 2023) and Moderate (Xia et al. 2023), and active learning basclines, such as AL (Park et al., 2022) and LDM-S [Coo et al. 2025).
For details on prompt generation baselines, data ensemble baselines, name-only standard learning setup, and implementatioon//ypereee /hyperparameters, see Sections A.3
A.9
A.10 and A.4 respectively.
5.2 Quantitative Analysis

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PACS
DomainNet
Method
ID
OOD
ID
OOD
Axuc
Alost
Axue
Atast
Axuc
Atast t
Axuc
Atoac
C2C (Prabiu al.
2014
17. 17.2942.75
33:3357775
28.3341.93
20.7741.51
35.062:0.41
27 27.81
11 11.9920.22 22
8.8220.05
Glide-Syn He e a.
2023
31.59+2.14
32 12.06
31 1.55
26.56+1.84
15.64=0.44
10 10.08++0.11 19
4.06+0.13 13
2.59+0.03
Real-Fale
Tuaau
2322
55.60:12.36
50.0012:220
28.6611.47
1..22:1.33
21.1320:26
18 19:9903330
6. 6.3320.11
1.5040.05
TE
[4 ot 21236
47.29++3.29
38, 90+-2.9
25. 24
18.23+1.87
34.76+0.52
27 27.55+00224
11 1..22+.22
8.50+0.14
Stadnring Saloo 2125
45.4242.61
41.15:12.42
27.10
1.63
32.58.10.53
27.24:10.22
10.47:10.28
7.05:10.11
Sato
39.9741.39
37.7812.75
24.1812.90
2..05511448
27.0111.82
20:3310:339
8.3510.51
6.5310.74
LE
He
2025
16:17:22000 00
45. 33
32. 32.1241.335
27. 27.56200.66
20.01:0.27
15 15.3830331
6.1022.13
1.5920.09
(+)
CONAN
49.37+3.77
50.46+
1..88+1.779
30.29++0.81
30.80=0.63
25 00:000
9.54+0.22
7.50+0.11
CHB
(unryidiz at al.
2023
47.52442.69
40.11++1007
31.02+1.111
22.62+1.61
16.09+0.10 16
13.45+0.19
5.61++0111
4.18+0005
(+)
CONAN
52.01.12.72
45.46.13.27
32.62.1.72
24.26.10.89
29.0610.37
24.5210.17
9.2810.14
T.56.10.14
SC
Tian ot al.
2024a
44.03++1105
41.48+3.05
3..12+1.19
23.07+1.04
11 11.80+0.11 17
8.60++0.20
3.90++0007
2.68+00044
(+) CONAN
52.35
31.0++1.66
23. 1.39
22 02.36=0.31
19. 13.1320.32
6.71=0.15
5.18+0.13
OCG [Hunmord et nl. 2024
45.49.12.88
19.19.11.69
30.20:11.1
21144410.71
12.55.10.22
10.21110:26
4.0310.10
2.9110.110
(+) CONAN
16.6543336
45. 7.21999
31.1+1188
25. 1.1
18 18.3240.42 42
15 83++0.31
5.78+0.17
1.70
HIRPG
51. $1.36:22.00 50
61, 11..33+2.000 19
31.12+1127
28. 1.31.32
27. 17.7220.30 30
23. 73.719
10:70#+.11 19
8. 767600011 13
(+) CONAN (Ours)
45.89-3.000
35.43+2.44
38.53+
33.73 1.82
35.60-0.0 00.31
29.99 -0.1
14.53 00.22
12.65 0.00
MA
67.10:14..07
61.95.10.92
2..75.11.44
20.90:1:.95
51.1310.28
42.95.10.15
13.48100099
10.60
Quantitative comparison between different name-only baselines on CIL setup. MA refers to training model with manually annotated data. The combination of HIRPG and CONAN refers
Table 2:
to our proposed GenOT. We bolded the highest accuracy, excluding the MA data (i.e., ideal scenario). We
provide details about ID and OOD sctups for each domain--enerrlization benchmark in Section
Bongard-HO1 HOI
Bongard OpenWorld
Method
Presiive/
Negative
Concept Answering
Preitive / Negative
Concept Answering
AAvo 1
Alast f
Aauo f
Alsst
Axuc 1
Atoxt"
AxUe f
Atust
C2C [Prably
2014
61.51:11138
59.58:12.40
33.8813.21
67.4011115
19.75.1:.49
50.3920.89
39.56113.58
67.56 6.756
IE
[L
62, 02.26=2.79
62.12+2.51
74.22+3.03
67.51+2.26
48. 79
51.05=0.72
68 88.95=2.35
68 08.8611.85
Sea Faring (Sata 2023
61.38-22.55
62.25+ 1.98
72.66++2.84
67.0242..66
49.06+-0.87
48.95+0096
06.76-2.72
65.20++1000 1.02
Tiara (aato 20
59 29.65-2.38
60.55+2.12
70.56+2.87
06.1442.776
47.0142.21
46.34#1.91
64.22-2.27
63.38+2.82
Glide-Syn cl al. 2025
54.83:22.07
55.77:43.54
67.87:13.30
59.38:13.62
LE (He et sl.
2023
64.03=3.10 10
62 02.40:2.588
73.65++3.60
70.68+3.80
(+) CONAN
65.00-22.5
65.63++2.59
74.99++3.07
72.384-2.76
HIRPG
67.25=2.61
71:19=0.12
75.5243.11 17
73.9743.11
18.37=1.17
47.4833477
70.09=1.92
71.59=3.11
(+) CONAN (Ours)
70.2013.97
73.1812.40
77.0113.45
75.80=1.83
53.68=1.18
57.7422.18
73.1013.79
76.7713.81
MA
69.50=1881
73.01#2.71
76.0243.85
70.37+3.87
53.11#1911
53.06=3.45
70.81=3.1
72.21=3.75
Table 3: Quantitative comparison between different name-only baselines on Multi-modal MV-
CIL setup. GenOT, outperforms baselines, as well as manual annotations (MA). We provide details on Positive/Nogative and Concept Answering tasks in Section A.1
Effectiveness of GenOL in name-only CIL setup. We compare models trained with GenOL, basclines, and MA data (oracle case), and summa- rize the results in Table D and Table 2 In the ID domain, MA outperforms baselines and GenOI, since the MA data test set is used as the test set in the ID domain, making the training and test sets come from the same domain, thus serving as an oracle.s However, in the OOD domain, GenOL outperforms both MA and baselines. We believe that GenOL achieves bet- ter generalization by generating more diverse images through HIRPG and CONAN. Additional compar- isons with various combinations of prompt generation baselines and ensemble methods are in Section A.31
ID
OOD
Method
Aauc 1
Alast
AAUC
Atast t
IE
9.94
8.26
6.25
5.10
C2C Glide-Sya
9.25
8.05
6,38
4.78
6.25
3.53
5.16
2.73
SC
13.92
10.65
5,83
4.38
LE
12.59
9.29
10.03
7.86
CCG
12.43
8.84
5.62
5.07
CIBB
12.30
9.14
7.19
6.29
CCG
12.43
8.84
5.62
5.07
GenOI. (Ours)
14.18
11.39
10.73
9.64
MA
30.01
25.03
6.90
4.95
Table 1: Qualitative comparison on ImageNet- R. GenOL outperforms baselines in the ID domain and even outperforms MA in the OOD domain.
Effectiveness of GenOL in name-only MVCIL sctup. We also empirically validate the effectiveness of GenOL in the name-only MVCIL setup and summarize the results in Table 3 CHB, SC, and CCG are excluded due to their focus on image classification, and Glide-Syn and LF, which use word-to-sentence
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PACS
DomsinNet
Full
Method
Dataset
ID
OOD
ID
OOD
Traiiin
Axuc 1
Alaxt 1
AAuc f
Alazt 1
AAuC 1
Assat f
AAUC
Abaa
CONAN (Ours)
55.89
55.43+22.99
38.53+ 1.14-11.1..
83.73+1.82
34.60-0.31
30.09+0.11
14.53-0022
12.65+00.09
No ensembling
31.36+22.9
51.63=2.40 49
31.12+1.27
28. 18:1 32
27. 07.30
23.71+0.33 39
10.70=00119
8.75#+0.13
EWS
50.56::2.32
50.03=2.11 13
14..911411
27.13:3.44 44
32.3820.47
26.45:00355
12.9320.28
10.9220.06
Moderate (CCL zwa))
47.03:+:352
45.34=1.11
35.06+22.03 03
27.91=2:17
25.07+0.42
10.36:00.16
10.53+0:29
8.1700.13
LDM-S JCCLR 2024)
53.16:::2.00 06
1..32=1.76
36 26..9222.10
26,32:1.00 01
30.6820.38
06.71
12:29:00:15
10, 10.05:20110
Uncertainty (ICLR 2020
39.75-4.2.10
31.17=33.89
32.00 1.42
25. 25:17:13
21.90/
15.70:40.08
10.01
7.19:40.11
CRAIG HCML 2020)
53.5742..13 13
54.21 01
35.5411.900
32,29 0.99
32.53100221
28.41110 23
13.25
11.53:0006
Glister (AAAI 2021)
40.55:1:2.43
37.75:13.81
34.301 1.66
27.56:11.31
23. 23.1610.37
16.98.10.35
10.561:0.28
7.60:10.18
GrudMateh (ICMI. 2022))
54.93+33.24
54.06+1.44
35.05+ 1.70
20.81-1.35
32.03+0.43
26.36+00441
13.48+0.31
11.74+0.18
Adacore (ICML 2022)
52.06:1:2.64
48.37 80
35.55.12.00
00.36.10.85
12.1510.55
26.83.10.18
13.62.10.27
11.17.0.04
(NorrPPW 2322)
54.40++ 1.57
52.82--2.30
36.98 1.86
30 30.44-1.99
28.02+0.33
25.78+00.0
10.55++0221
9.24+0.14
LCMat (AIS IXIS 2013)
53. 0334032333
54,60=1 655
35.37+1.02
30 30.04=0.82
32.38+0044
28 08.36++0.22
13.42=0026
11.7670.17
Table 4:
Quantitative comparison between data selection methods. EWS combines generated data from multiple models in equal proportions, while No Ensembling uses a single model (i.e., SDXL).
models, are inapplicable to Bongard-OpenWorld OpenWorld, which has sentence-form concepts. As shown in the table, GenOL effectively trains LLaVA-15-7BB outperforming baselines and even compared to MA data.
Note that while C2C, IE, and MA data utilize human-annotated hard negative concepts (e.g., 'hold a bike") alongside positive concepts (e.g., 'ride a bike'), GenOI. relies solely on positive concepts. Specifically, for MA data, high-quality annotators from Amazon Mechanical Turk were employed to select hard negative examples and filter out noisy data (Jiang et al., 2022). For web-scraped data, long-context queries (e.g.. hard negalive images of riding bike') often retrieve noisy images, thus, it requires manually annolated negative concepts instead. In contrast, GenOL automatically selects relevant hard negative concepts based on the given concept, leveraging commonsense priors from LLMs (Zhao et al.) 2023 Yang et al. 2024).
Even without manual annotation, GenOL outperforms models trained with both MA and web-scraped data by leveraging LLMs' commonsense prior and the controllability of T2I generative models (Nic et al. 2021 We provide prompts that we employ to select hard negative concepts in Section A.6
Effectiveness of GenOL in online learning setup. We further validate that GenOL outperforms bascline methods even in online learning setups without distribution shifts (i.e., where all target concepts are presented at once). We provide detailed results in Sec. A.28 for space's sake.
Comparison of HIRPG with Prompt Generation Methods. To validate the effectiveness of HIRPG
in diverse prompt generation, we compare models trained on data generated from prompts derived by prompt generation baselines (LE, CHB, SC, and CCG). As shown in Table 2 HIRPG significantly outperforms the baselines, with and without CONAN. We attribute this improvement to two key components of HIRPG: recurrent prompt generation (RPG), which mitigates prompt overlap, and hierarchical generation (HIG), which alleviates the lost-in-thee middd- issue (Liu et al. 2023c) that arises with RPG alone. An ablation of
these components, along with Diversity (Naeem et al. 2020) and Recognizability (Fan et al. 2024] analyses, is provided in Section A.3
Comparison of CONAN with Data Ensemble Methods. To demonstrate the effectiveness of CONAN, we compare it with existing data ensemble methods, as well the equal-weight selection (FWS). For fair comparison, we ensure an equal number of selocted images in the ensemble set across all ensemble methods. After data selection, we evaluate the performance of continual learners trained with each ensemble set and summarize the results in Table 4 For sample selection, we use a CLIP-pretrained ResNet-50 as the feature extractor 9, following Cui et al. (2023). Note that Uncertainty, Glister, GradMatch, and LCMat require fine-tuning the feature extractor on the full dataset for gradient calculations, even with a pre-trained feature extractor. As a result, for these baselines, we fine-tune g for 30 epochs using the full dataset. In contrast. Moderate and CONAN do not require fine-tuning, as they only use g to calculate feature distances. Despite being training-free, CONAN outperforms methods that require finc-tuning, well as Moderate.
5.3 Analysis of Bias
We analyze gender, race, and geographical bias in web-scraped data (i.e., C2C) and generated data from our proposed GenOT. and other generative baselines (i.., LEE, CHB, SC, and CCG), and manually annotated
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MA
C2C(Web-scraped)
LE
35.9%
33.3%
44.17
55.9%
64.1%
66.7%
CHB
SC
CCG
GenOL(ours)
4.1%
35.9%
33.0%
44.6%
55.4%
64.1%
67. 17.0%
95.9%
Male
Female
Figure 4: Comparison of gender bias. GenOL generates data with reduced gender bias compared to baselines, and even compared to MA data.
LE
CHB
SC
CCG
GenOL(ours)
8.1%
14.2%
11.9%
17.1%
27.0%
64.9%
22.6%
63.2%
63.5%
22.0%
60.9%
White
Black
Asian
Figure 5: Comparison of race bias. GenOL generates data with reduced race bias compared basclines.
(MA) data. To measure bias, we calculate the similarity between embeddings of gender/rase attribution keywords and image embeddings, following Mandal al. (2023) Attribution keywords summarized in Sec. A.20 Based on this similarity, assign each image to its closest gender or race category and compare the number of images across these categories, following Wan et al. (2024) He et al. (2024).
Gender/Race Bias. We first compare gender bias among GenOL, web-scraped data (i.e., C2C), and manually annotated (MA) data, and summarize the results in Figure As shown in the figure, GenOT. exhibits less bias in both gender and race compared to C2C and MA. Next, we compare the race biases with generative baselines, including GenOL, and summarize the results in Figure D As depicted in the figure, GenOL demonstrates the least bias compared to other generative baselines. We attribute this to our proposed components, HIRPG and CONAN, which successively enhance intra- and inter-diversity of generated data, respectively, thereby mitigating bias issues. To this end, we believe that training with data generated by GenOL unlikely to introduce significant biases, even compared to MA data.
We also measure the geographical bias of objects, evaluating whether the generated content reflects artifacts and surroundings from across the globe, rather than disproportionately representing certain regions Basu et al. (2033) We provide the results in Section A.20 for spaces' sake.
Geographical bias.
5.4 Qualitative Analysis
We qualitatively compare data acquisition methods in the Bongard-HOI benchmark in Figure 6 Although the desired positive images are related to 'ride bike', C2C, a web-scrapping baseline, includes irrelevant images, such as 'sitting on a bike' or a road with a bicycle symbol, in the positive set. These noises can significantly hinder model performance (Kim et al. 2021 Bang et al. (022). Tn contrast, GenOT, effectively generates the desired output and hard negative examples through text descriptions, leveraging the controllability NNie al. 2021 of the generative model, thus generating results similar to MA data.
5.5 Comparison of Computational and Memory Cost
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Positive
Negative
MA
GenOL(Ourr) Web
Figure 6: Samples using different data acquisition methods for the same concept in the MVCIL setup. The given concept 'ride a bike' from the Bongard-HOI benchmark. The left. four images represent positive examples that depict the given concept, while the right four images represent negative examples that illustrate different concepts. Here, 'MA' refers to manually annotated data.
We compare computation and memory cost, and extra resources in Table 5 MA data requires 3,000,000 minutes of hurnan working hours to filter out outliers. Web scraping baselines (ie., C2C and IE) do not require any GPU resources; instcad, they rely web browsers for crawling. Generative baselines, such Glide-Syn, LE, CHB, SC, CCG, and our proposed GenOL, utilize 32 RTX 4090 GPUs for image generation. Additionally, these methods require 12GB of memory to store the weights of generative models. The major reason for the lower wall time of GenOL compared to other generative baselines the of class-agnostic prompts, which make its prompt generation time independent of the total number of concepts, mentioned in Section A.22
Method
Wall time (min)
Storage (GB)
Extra resources
MA
3,000,000
Human annotation
330
Web browsers
C2C
Glide-Syn
254
4.5
32 x RTX 4090 GPUs
Reke-Fake
LE CHB
240
222
SC CCG GenOL
952
RTX 4090 GPUs
952
182
Table 5: Comparison of computational and memory bud- get, and extra resources for acquiring DomainNet.
5.6 Ablation Study
We conduct an ablation study on two components of GenOL, i.e., HIRPG and CONAN using the ImageNet- pretrained ViT-Base models, and summarize the results in Table 0 Our findings show that both compo- nents significantly enhance ID and OOD domain performance, Additional ablation studies using ResNet-18 and HIRPG (comprising HIG and RPG) provided in Sections
A.7 and A.8 respectively.
We provide detailed studies in the appendix for the space's sake.
Specifically, explore fine-grained qualitative analysis of both prompts generated by HIRPG (Section A.34 and images generated by GenOL (Section A.29 and details of RMD
benchmarks (Section A.21 hyperparameter analysis (Section A.23
calculation (Section A.32
6 Conclusion
Online CL represents a practical learning paradigm, but the assumption of well-curated and annotated data limits its real-world applicability. To address this, we introduce a unified name-only CL frame- work that integrates generators with the continual learner, termed 'Generative name only Online Learning' (GenOL). Within the GenOL framework, we propose diverse prompt generation method (í.e., HIRPG) and complexity-guided ensembling (i.e., CONAN). Extensive experiments validate the performance im-
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Published in Transactions on Machine Learning Research (10/2025)
PACS
DomainNet
Method
OOD
OOD
AAUC:
Ause
Aaue
Alset
AAuc:
AAui T
Alooe 1
Vanilla GenOL
72.91=1.00 10
56, 86,25222688
10.39:1.00 67
27. 27.11=2.90
30. 30.96:0.31
22 22522+0.00 466
11.17=0:25
7.78=0.21
(+) HIRPG
78.5241.90
22.0022.40
15.1611.59
36.76-2.45
37.9040.31
30.3740.44
15.30=0:19
11.31=0.29
(+) CONAN
77.31=1.58
61.39+2.40
18.01=2.05
85.22=2.64
37.81+0.47
30.15+0.25
14.61=0.29
10.83=0.20
HIRPG & CONAN (Ours)
79.32+1.97
72.46+0.42
58.88+1.57
41.31 +2.42
42.73+0.25
26.09+0.. 0.50
18.6440.28
14.68+0.16
Table 6: Ablations for proposed components of GenOL. Vanilla GenOL denotes generating 50 different
prompls using an LLM without employing RPG HIG, and using single T2I generator, i.e., SDXL.
provements achieved by both components of the GenOL framework, demonstrating its effectiveness in both ID and OOD domains compared to webly-supervised and human supervision.
7 Limitations and Future Work
While GenOL replaces manual annotation by generating diverse images that only require concepts using HTRGG and CONAN, we acknowledge the limitations arising from the use of generative models. Here, we outline these limitations and propose future directions to address them.
Scalability. Recent studies (Yuan et al. 2024 Hammmoud et al. 2024 demonstrate that training with text-to-image generated samples (10-100x more than real data) achieves comparable performance to real data training. However, such scaling is impractical for name-only CL setup, as they require substantial time and computational cost, while CL requires fast adaptation (Koh et al. 2021 Ghunaim et al., 2023). In practice, there are various applications where a few manually annotated samples are provided along with concept names. Prior work Yuan et al. (2024) demonstrates that training with mixed real and synthetic data enhances performance while reducing computational costs. Therefore, future work could invest combining real data with GenOL-generated data to address scalability challenges more effectively.
Privacy and Copyright Concerns. Generative models prevent potential data lcakage in replay-basod CL by eliminating the need to store real data in episodic memory (Shin et al., 2017 Liu et al. 2024 However, they introduce privacy concerns through potential memorization of training data (Wang et al.
2023 Carlini et al.] 2023). To address immediate risks, we exclude person-related generated data from our relcased dataset. We believe that advancing privacy-preserving (Xu et al.] [2023 Chen & Yan. 2024] and differential privacy (Chen et al. 2023a) generative models would effectively mitigale these concerns.
Capability of Generation. As mentioned in Section D GenOL and other generative basclines focus on name-only CL scenarios where new concepts are new to specific systems or domains, rather than entirely novel concepts globally. Therefore, we leverage generative models' large coverage of everyday concepts, but this reliance limits their performance on completely new concepts absent from their training data
To handle such entirely unseen concepts, generative models require continual adaptation with few real examples of the new concept. As shown in Section A.26 even for previously seen concepts, continual adap- tation of the generative model with a few real data significantly improved both ID and OOD performance. By extension, we argue that providing a few real samples for completely unseen concepts can enable contin- ual training of generative models, thereby generating abundant synthetic data for these new concepts. We believe that advances in efficient continual training methods for generative models (Smith et al. 2024 Song et al. 2024a Uehara et al. 2024 will further accelerate GenOL 's ability to accommodale unseen concepts.
Moreover, GenOL 's flexibility, allowing for seamless replacement of the underlying foundation model, ensures it evolves alongside advances in foundation models (e.g., GPT-3.5 + GPT-4o - GPT-5). This expansion enables the system to acquire data for an increasingly broad range of concepts over time. As an example of the flexibility, Section [A.23] shows that even when GPT-4o is replaced with LLaMA3-SB or Qwen3-8B, GenOL consistently outperforms the baselines, demonstrating its potential to sustain strong performance with future models. In other words, while GenOL does not directly implement continual adaptation, its flexible design ensures it advances alongside evolving foundation models, evolves alongside evolving foundation models.
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8 Acknowledgement
This work was partly supported by the IITP grants NN.RR-2002--T222007,, No.RS-2022-TT2201 No.RS- 2022-I1220959, No. No.RS-2002-11220877 No.RS-2021-1221143 (SNU AI), R0S-2002--1222008 (AI Innov. Hub), No. R8S-025-255423338 (AT Star Fellowship-SNU) RSS-002-TT2220511 funded by the Korea government (MSIT) and a grant Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea (RS-2025-25424633);
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A Appendix
A.1 Details about Multi-modal Visual-Concept Incremental Learning Setup
Beyond CIL sctups, we also assess GenOL in multimodal tasks, such as context-dependent visual reasoning tasks, focusing on Bongard-HOO (Jiang et al. 2022) and Bongard-OpenWorrl (Wu et al. 2024a). These benchmarks are based on two desirable characteristies of classical Bongard problems: (1) few-shot concept learning and (2) context-dependent reasoning. The former refers to the ability to induce visual concepts from a small number of examples, while the latter indicates that the label of a query image may vary depending on the given context. (i.e,; positive and negative support set). Specifically, as shown in Figures andd S given a positive support set and a negative support set for a particular concept (e.g., 'ride bike" J. we consider two types of tasks that address the following queries: (1) " "What is the concept exclusively depicted by the positive support set?" and (2) "Given query image, does the query image belong to the positive negative support set?" We refer to these tasks as CA (Concept Answering) and P/N, respectively. In addition, we provide a detailed description of each visual concept reasoning benchmark.
Bongard-HOI (Jiang et al. 2022). Bongard-HOP denotes concept = (a, as visual relationship tuple, where a, o are the class labels of action and object, respectively. Following Bongard's characteristic, there are positive support set Ip and negative support set In, where In and In have different concepts. Specifically, if the concept of Ip is (a, c), In is composed of data with concept d' = (a.o), where a * a. As a result, images from both In and Ip contain the same categories of objects, with the only difference being the action labels, making it impossible to trivially distinguish positive images from negative ones through visual recognition of object categories alone (i.e., hard negative examples). We provide examples of Hongard-HOI-CA & Bongard-HOL-P/A (Jiang et al. 2022 in Figure 7
-angard-OppeWorld (Wu et al. 2024a). In contrast to Bongard-HOI, which has structured concept crepresented as (action, object), Bongard- Congard-OpenWorld utilizes free-form sentence to describe the content depicted by all images in the positive set Ip exclusively. Specifically, concepts are obtained by the annotators, who are instructed to write visual concepts by following predefined set of categories. We provide examples of Bongard--OpenWoord-CAA & Bongard-OpenWorld-PP
(Wu et al. 2024ah in Figure
Note that since the input consists of both text queries and images (L.e. support sets and a query image) and outputs are sentences, we use multimodal large language models (MLLMs), such as LLaVA (Liu et al., 2023b), which connects vision encoder with an LLM for general-uurpose visual and language understanding. For further implementation details, such as the prompts we use, see Section A.4
A.2 Details about Experiment Setup
To set domain generalization benchmarks (i.e., PACS (Zhou et al. 2020 DomainNet (Nysshaurr et al., 2020), ImageNet-R (Hendrycks et al. 2021 and CIFAR- W (Sun et al. 2024 for class incremental learning (CII.) setup, we divide it into multiple disjoint tasks. We assume disjoint setup (Parisi et al. 2019). where tasks do not share classes. We summarize the in-distribution (ID) domain, the out-of-distribution (OOD) domains, the total number of classes per dataset, the number of classes per task, and the number of tasks for each dataset in Table 7 Within each dataset, all tasks have the same size, except. PACS, which has a total of classes. For PACS, the first task includes classcs, while the subsequent tasks include data for 2 classes each. For CIFAR-10-W, even though CIFAR-10 Krizhhevsky resolution of CIFAR-10 is 32x32, while CIFAR-10- W has resolution of 224x224, leading to performance degradation. Therefore, we exclude comparison with MA in the CIFAR-10-W experiments. For multi-modal visual-concept incremental learning (MVCIL) setup, we summarize the total number of concepts, number of tasks, and number of concepts per task in Table
al. (2009) can use MA data, the image
Note that we run five different task splits using five different random soeds and report the average and standard error of the mcan (SEM) for all experiments, except for ImageNet-BR R due to computational resource
constraints, following (Koh et al. 2023 Seo al. 2024).
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Positive Support Set
Negative Support Set
Query Images
Tasks
CA
Concept exclusively depicted by positive set?
P/N
Query image belongs to positive or negative set?
Ground truth concept walk dog
Labels:
Positive
Negative
Figure 7: An example of the Bongard-HOI task. CA refers to the concept answering task, while P/N refers to the classifying whether a query image belongs to the positive or negative set.
Dataset
ID domain
OOD domains
total of classes of tasks # of classes task
PACS
Photo
Art, Cartoon, Sketch CIFAR-10-W
One task:
Others:
CIFAR-10-W
10
DomainNet
Real
Clipart, Painting, Sketch
345
ImageNet-R
ImageNet
ImageNet-R
1000
5
20
Table 7: Task configurations for the CIL setup on each domain generalization dataset. We split
Dataset
Form of Concepts
total # of concepts
# of tasks
# of concepts / task
Bongard-OpenWorld
Free-form (action, object)
10
2
Bongard-HOO
50
10
Table 8: Task configurations for the MVCI setup on each Bongard benchmark.
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Positive Support Set
Negative Support Set
Query Images
Tasks
CA
Concept exclusively depicted by positive set?
P/N
Query image belongs to positive or negative set?
Ground truth concept a: The top of a snow covered mountain.
Labels:
Positive
Negative
Figure An example of the Bongard-OpenWorld task. CA refers the concept answering task, while P/N refers to the classifying whether query image belongs to the positive or negative set. The concept is free-form, such as sentences.
A.3 Prompt Diversification and Concept-specific Prompt Generation Baselines
For a fair comparison, we used the same number of diversified prompts (t.e., 50) for all prompt diversification methods, including our proposed HIRPG Morcover, for all LLMM-based prompt generators, consistently used GPT-4o Ww et al. 2024b) as LLM.
LE He et al.) 2023). LE leverages an off- the-shelf word-to-sentence T5 model, pre-trained on the "Colos- sal Clean Crawled Corpus" dataset. (Raflel et al. 2020)) and fine-tuned on the CommonGen dataset {Lin al., 2020), to increase the diversity of language prompts and generated images, with the aim of better harnessing the potential of synthesized data. Specifically, the concepts are entered into the word-to-senlence model, which generates diversified sentences containing the concept. These diversified sentences are then used prompts for the text-to-image generation process.
CCG (Hammoud et al. 2024). For given concopt set C, CCG (Concept-based Captions Generation) uses an LLM generator GLLM produce concept-specific prompts for T2I models. The designed prompt p for each concept E C. is structured as follows:
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Your task is to write me an image caption that includes and visually describes a scene around concept. Your concept is Output one single grammatically correct caption that is longer than 15 words. Do not output any notes, word counts, facts, etc. Output one single sentence only.
Formally, the set of generated captions for concept can be defined as T= (le, GLLM(P, c)), Ve C, Vn e (1, N}; where N is the number of desired prompts for each concept.
CHB (Sariyıldız et al., 2023). To increase the visual diversity of the output images, CHB (Concept Name Hypernym + Background) combines background information along with hypernyms, which helps reduce semantic ambiguity. They assume that class can be seen 'inside' a to enhance visual diversity, CHB combines the concept name and its hypernym (as defined by the Word- Net (Miller 1995 graph) with scene classes from the Places365 dataset (iópett et al. 2020) as background for each concept. Formally, Pa. can be defined as "P= = c, he inside b", where refers to the concept name, he refers to the hypernym of the concept c, and b refers to the background.
background. Therefore,
SC Tian et al., 2024a). SC (Synthesizing Captions) considers three types of templates for each concept c: > caption, bc) - caption, and (c, rel) + caption.
caption. They generate a sentence directly from the concept name using LLM.
(c. bg) caption. Similar to CHB (Saryyildız al. 2023 they combine the visual concept with back- ground by. However, while CHB randomly selects by from the Places365 dataset, they generate a list of suitable backgrounds for the chosen concepts using LLM, which helps avoid unlikely combinations, such as "a blue whale on a football field".
rc,rel) + caption. They consider pairing given visual concept with a positional relationship word rel, such as "in front of". To add variety, rel is randomly selected from a predefined set of 10 relationship words. Using LLM, they then generate captions that reflect the selected relationship word in relation to the concept.
Real-Fake YYuan et al. 2024). Real-Fake aligns both data and concept-conditional distributions through Maximum Mean Discrepancy (MMD))-ased loss (Gretton et al. 2006) to minimize the discrep- ancy between real and synthetic data distributions. For prompt generation, leverages BLIP2 (Li et al.) to generale captions that. incorporate concept-relevant information. These captions are combined with concept names and intra-concept visual features guide the generation process.
IE (Li et al., 2023b). TE (Internet Explorer) dynamically queries search engines by combining concepts from the WordNet hierarchy (Miller 1995) with descriptors generated by GPT-J (Wang & Komatsuzakii 2021). For instance, the concept 'duck' can be combined with descriptors like 'baby' or 'red', resulting in queries such as "baby duck' or 'red duck'. The descriptors are generated using prompt template like "The {concept] is [descriptor]" and sampled with temperatures to ensure diversity. The retrieved images are filtered based on their similarity to the target dataset using a relevance reward metric.
A.4 Implementation Details
We use ResNet18 (He al 2016) and Vision Transformer (VIT) (Dosovitskiy & Brox 2016 network architectures for the dlass-incremente learning (CIL) setup. Due to the large number of parameters in ViT, training it from scratch in an online setup resulted in lower performance. Therefore, we used the weights of a model pre-trained on ImageNet-1K3 (Russakovsky et al. 2015) as initial weights for ViT. For data augmentation, we consistently applied Randengment (Cubuuk et al. 2020 all experiments. For task split, we adopt disjoint sctup, where tasks do not share classes (Parisi al. 2019). We used the GPT-4 model (Achiam et al. 2023) for all ALM-based prompt generation baselines, including HIRPG, for a fair comparison. Furthermore, to ensure fair comparison among manually annotated data, generated data, and
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web-scraped data, used equal mumber of data in all experiments. Regarding the web-scraped data, we first acquire 20% more samples than necessary to filter out noisy data, following (Prabhu et al. 2024) To achieve this, we utilized pre-trained CLIP (Radford et al. (021) for filtering, which excludes the most noisy bottom samples, resulting in a cleaned subset used for training, following (Schuhmann et al. 2022) We used 8x XTTTX 4090 GPUs to generate images using texl-lo-image generative models.
Prompts. For the prompt diversification module v we use the following system prompt to sequentially generate the prompts:
To generate images using text-to-image generation model, need to create prompt. Keep the domain photorealistic and use different visual scenes visual styles or different color profiles/ palettes. Here is list of prompts that I have previously generated <previously generated prompts>. Please create a new prompt that does not overlap with these.
In Bongard-OpenWoolddPP/N, we the following prompl:
'positive' <mages:<image>.. <image>
'negative' :iagges<<mage>> <image>
'query' <imgee<image>
Given 'positive' images and 'negative' images, where 'positive' images share 'common' visual concepts and 'negative' images cannot, the 'common' visual concepts exclusively depicted by 'positive' images. Here, 'common' sentence from 'positive' images common concept. And then given 1 'query' image, please determine whether it belongs to 'positive' or 'negative'.
Your answer is:
In Bongard.OpenWorld-CA, we use the following prompt:
'positive' images: <image>...........
<image>
'negative' images: <image>.... ... <image>
Given 4 'positive' images and 'negative' images, where 'positive' images can be summarized 'common' sentence and 'negative' images cannot, the 'common' sentence describes of concepts that are common to 'positive' images. Please give the 'common' sentence from 'positive' images.
Your answer
The system prompt Ps for HIRPG we use is as follows:
To generate images using text-to-image generative model, need to create prompt
Here is a list of prompts that have previously generated. Please create new prompt that does not overlap with {base prompt & previously generated prompts}
In Ho1-/rd--HO1PP/, the following prompt:
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'positive' images: <image>. <image>
'negative' images: <image>... <image>
"query' image: <image>
Given 2 'positive' images and 'negative' images, where both 'positive' and 'negative' images share 'common' object, and only 'positive' images share 'common' action whereas 'negative' images have different actions compared the 'positive' images, the 'common' action is exclusively depicted by the 'positive' images. And then given 1 'query' image, please determine whether belongs to 'positive' or 'negative' must choose your answer from the Choice List. Choice list: (Positive, Negative].
Your answer is:
In Bongard-HOI-CA, we use the following prompt:
'pasitive' images: <image>......................... <image>
'negative' images: <image>... images:<image>... <image>
Given 2 'positive' images and 2 'negative' images, where both 'positive' and 'negative' images share 'common' object, and only 'positive' images share 'common' action whereas 'negative' images have different actions compared to the 'positive' images, the 'common' action is exclusively depicted by the 'positive' images. Your job is to find the 'common' action within the 'positive' images. You must choose your answer from the Choice List. Choice List: [choice lists].
Your answer is:
Hyperparameters. For т, which refers to the temperature the softmax function in CONAN, is uni- formly set 0.5 across all datasets. For L, the truncation ratio used in RMD score normalization, we set it to 5% for all experiments. For díverse prompt generation basclines, different prompts for all baselines across all benchmarks, including HIRPG, to ensure fair comparison. Specifically, in GenOL, we set depth D = 2, and K 7 for all setups to generate 50 prompts using HIRPG. For the optimizer and the learning rate (LR) scheduler in the CIL setup, we employed the Adam optimizer with an initial LR of 0.0003 and the Constant LR scheduler, respectively, following prior works (Koh et al. 2023 Seo al. 2024). In the MVCIL setup, we use Adam optimizer with LR 10-5 and Constant LR. scheduler.
well GenOL, generate 50
Following Koh et al. 2021 2023 Kim et al. 2024a), conduct batch training for each incoming sample. Specifically, for PACS, CIFA R-10 Domain Net, and ImageNet, the number of batch iterations per incoming sample is set to 2, 2, 3, and 0.5, respectively, with batch sizes of 16, 16, 128, and 256. Episodic memory sizes are configured as 200, 2000, 10000, and 80000 for PACS, CIFAR-10-W, DomainNet, and ImageNet, respectively.
For MVCIL sctups, the number of batch itcrations per incoming sample is set to 0.5, with a batch size of and a memory size of 500 in both Bongard-HOI and Bongard-OpenWorrd. Unlike the CIL setup, where data is composed solely of image and label pairs, in the MVCIL setup, each set contains both negative and positive examples corresponding to a given concept. Therefore, we store 500 sets in episodic memory. In MVCIL benchmarks, í.e., Bongard-HOO and Bongard-OpenWorrl we used positive images and 2 negative images for a support set and positive images and negative images for a support set, respectively. For the MVCIL setup, we use the A.L.VA-155 (Luu et al. 2023b
We provide hyperparameter analysis of temperature т, depth and width hicrarchical tree structure, and truncate ration I. in Section A.23
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A.5 Comparison of HIRPG with Diverse Prompt Generation Methods.
To evaluate the effectiveness of HIRPG in diverse prompt generation, we further qualitatively analyze the generated images based two metrics: Recognizability (aan et al. 2024p. which evaluates whether the images accurately represent the intended concepts, and Diversity (Nacem et al. 2020), which assesses the variation between images. While our goal is to generate diverse images using varied prompts, it is important that the generated images accurately represent the desired concepts. Thus, the prompts must achieve both diversity and recognizability. For a fair comparison, we generate 50 text prompts using each prompt diversification basclinc and use SDXL to gencrate the same number of images for all basclines, including HIPGG, as mentioned in Section A.4 We summarize the results in Table g
PACS
DomainNet
Method
Recognizability
Diversity
Recognizability
Diversity t
LE (He al. 2023
65.39
0.27
38.49
0.31
CHIB (Saryyıldz al.
2023
62.96
0.16
41.57
0.24
SC
TTan et al. 2024a
71.50
0.19
33.19
0.20
CCG
HHmmmuu et al.
2024
68.78
0.18
32.71
0.19
HIRPG (Ours)
90.77
0.31
52.83
0.35
Comparison of prompt diversification methods. We compare the Recognizability and Diver- sity of images generated using text prompts derived from prompt generation methods conjunction with a text-to-image generative model.
Table 9:
As shown in the table, HIRPG not only generates more diverse data, but also produces more recognizable data compared to baselines. Overall, DomainNet exhibits higher Diversity. This is because it has fewer images per class, resulting a smaller number of generated images per prompt. For detailed descriptions of the baselines and metrics (f.e., Rec and Div), see Sections A.3 and A.13 respectively.
A.6 Selecting Hard Negative Concepts in GenOL on MVCIL Setups
In the MVCIL setup, which requires negative concepts to learn concept, MA data and web-scraping baselines (i.e., C2C and TE) use manually selected hard negative concepts. In contrast, we leverage L.MSs hard negative sample selection capability (Moreira et al. 2024] to eliminate the need for human annotation. We provide prompts that we used for selecting hard negative concopts below.
For the Bongard-HOI benchmark, Given a (object, concept) pair, e.g., (ride, bike), GenOT retrieves hard negative concept using an LLM and the following prompt:
To train model that distinguishes between positive and negative images, you need choose N negative actions from the following negative action list. When choosing negative actions, you should consider the available actions from the object. For example, if the object is 'bird', possible actions are "chase", 'feed', 'no interaction', 'watch' etc. If the object is 'orange', possible actions are 'cut', 'hold', 'no interaction', 'peel', etc. You should choose hard negative actions that are clearly distinguishable from positive actions among the possible actions. object: <object class>
positive action: <positive action> negative action list: <action set
Please select a total of negative actions. The response format must strictly result: ['negative actionl', 'negative action2', J. and all negative actions must be included in the negative action list.
For the Bongard-OpenWorle OpenWorld benchmark, GenOL retrieves hard negative concept using an LLM and the following prompt:
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To create image using text-to-image generation model, want create prompt. Below, prompt for positive image will be provided, and the goal is to generate prompt for negative image. It is important that the negative prompt partially overlaps with the positive prompt and has slight differences. For example, if the positive prompt is 'Dogs are running', then 'Dogs are drinking water' would be the negative prompt. Please create N 'negative' prompt sentences (under words) that fits this description. Please ensure the response format strictly prompt: answer'.
Positive prompt: <positive prompt>.
A.7 Additional Ablation Study of GenOL
In addition to Section 5.6
conduct ablation study two components of GcnOL, namely HIRPG and CONAN, using the ResNet-18 model. We use the same number of images for each baseline to ensure fair comparison, and summarize the results in Table 10
Similar to the ablation study with ViT-base, both components significantly enhance performance both ID and OOD domains.
PACS
DomainNet
Method
ID
OOD
ID
OOD
AAti
Aiost
AAUC
Absat 1
AAUC
Aiast t
AAtic t
Atuss 1
Vanilla GenOL
47.74
47.3012.38
11.6611.45
25.41=0.66
20.82410399
17.1910.34
7.0910021
5.55.00.11
(+)
HIRPG
51. 36:2.599
31.63422.19
31.12=127
28.18=1.32
27.72=0.30
23.71+0.39
10.70=0.19
8.75=00.13
(+] CONAN
50.02-+2.52
45.34444.25
33.94+1.3 1.37
27.30-1.10 1.16
28.1740.38
24.1240.11
9.76+0.11
8.18+00:15
HIRPG CONAN (Ours)
65.89:33.06
55.43=2.49
38.53=1.15
33.73+1.82
31.60+0.31
29.90:0.11
14.53:0.22
12.65::0.09
Table 10: Ablations for proposed components of GenOL. Vanilla GenOL refers to generating 50
different prompts using an LLM without employing RPG or HIG, and using single T2I generator, i.e.,
SDXL. We use ResNet-18 as backbone architecture.
A.8 Ablation Study of HIRPG
We conduct ablation study HIRPG to investigate the benefits of each proposed component, hierarchical generation (HTG) and recurrent prompt generation (RPG), in PACS and DomainNet. For fair comparison, we generate 50 different prompts and use SDXL to generate images for all baselines. For HIG, we use a 7-ary tree with a depth of 2, the same configuration as in HIRPG
The results are summarized Table D In the table, vanilla prompt generation refers to generating N different prompts using LLM without applying RPG or HIG. Specifically, use the following prompts for vanilla prompt generation:
To generate images using text-to-image generation model, need to create 50 prompts. Keep the domain photorealistic and use different visual scenes visual styles or different color profiles/ palettes. Please create prompts that do not overlap with each other. Please ensure that each response includes the word '[concept]" For example, photo of a [concept].' 'A detailed sketch of [concept].", ; hyper-realistic portrait [concept].', etc.
As shown in the table, applying RPG alone even degrades the performance compared to vanilla prompt generalion. This degradation occurs because, as iterative steps progress, the length of the LLM input increases, making challenging to utilize the information within the extended context effectively (i.e., lost- mn-the-mddlle challenge (Liu et al. 2023cc An et al., (2024), as discussed Section In contrast, combining RPG with HIG addresses the lengthy input problem, leading to significantly improved performance in both in-distribution (TD) and out-of-distribution PACS and DomainNet..
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PACS
DomainNet
Method
ID
OOD
ID
OOD
Axuo 1
Atook 1
AAUC
Ataa
AAUC 1
Atust
Axuc
Atost
Vanila Prompt Generation
4..74+11.52
47.30++2.36
1.466.144
25.41=0.66
20.82+0.39
17.19400.4
7.09=0.21
6.65+0.11
(+) RPG
15.35+11.55
47.0 17.00)+1..00
32.07=1.70
25.13=1.71
17.62=0.35
13.96:0.25
6.88=0.15
5.30=0.11
HIG RPG (Ours
51.36=2.59
51.6312.49
31.12:1.27
28.1841.82
27.72=0.30
23.71=0.39
10.70=0.19
8.75=0.13
Table 11: Benefits of components of the proposed prompt generation method. RPG refers to the recurrent prompt generation, and HIG refers to the hierarchical generation. Vanilla prompt generation refers to generating 50 different prompts using an LLM without incorporating RPG or HIG.
A.9 Data Ensemble Baselines
Gradient-based methods (Killansetty al 22021b Shin et al. 2023 minimize the distance between the gradients from the entire dataset T and from the selected coresct S (S T) follows:
Vol(o, T
UA Vol y; l/w|12
min
Σ
(5)
(N.Y)ET
(x,y)ES
where w is the vector of learnable weights for the data in selected coreset S, refers to the loss function, denotes the model parameters, and || Il, || represent the L1 norm and L2 norm, respectively. To solve Equation GradMatch (Killamsetty et al. (2221a) uses orthogonal matching pursuit algorithm (Flenberg
et al. 2016 while CRAIG (Mirzasoleiman al. 2020) uses submodular maximization.
LCMat (Shin et al., 2023). . While Craig and GradMatch minimize the gradient difference between T and the S, LCMat matches the loss curvatures of the and over the model parameter space, inspired by the observation that a loss function
quantifies the fitness of the model parameters A under a specific dataset. Specifically, they claim that even though optimizing S toward T with respect 0 would decrease the loss difference between T and S in 0 (f.e., [LT; L(S:))), if the loss difference increases with small e,
perturbation in 0 (f.e., [T;; + +) L(S;0 e)), it indicates a lack of generalization over-fitted reduction of S by A. Since this generalization failure on the locality of 0 subsequently results in the large difference of loss surfaces between and S, they propose an objective that maximize the robustness of under perturbation e as follows:
min (T, (T,
(6)
where Taabs refers the loss difference between and S on (i.e., - -((:)))
Moderate Xia et al. 2023 Moderate selects data points with scores close to the score median, using the median as proxy for the score distribution in statistics.
Specifically, given a well-trained feature extractor f(-) and the full training data T = {t1, t2...... , process begins by computing the hidden representations (or embeddings) of all data points in T, i.c., {ar = [(11), 22 = (122)...:; An = f((.)). Next, the &2 distance from the hidden representation of each data point to the class prototype of its corresponding class is calculated. Formally, for a sample belonging class e, its distance d(t) given by d(t) ||z 2²]12, where z f(t) and 2" is the prototype of class c. Subsequently, all data points sorted in ascending order according to their distance, which are denoted by {d([1), d((2), d((2)...,, d(T..)). Finally, data points near the distance median are selected as the coreset S.
tx), the
Uncertainty (Coleman et al., 2020). Uncertainty suggests that data samples with a lower level of confidence in model prodictions will have greater influence on the formation of the decision boundary. For uncertainty scores, we utilize Entropy, following the approach of Shin al. (003)), among the methods LeastConffidence, Entropy, and Margin.
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Glister (Killamsetty et al., 2021b). Glister is generalization-base data selection method that opti- mizes generalization error via a bi-level optimization problem to select the coreset S, aiming to maximize the log-likelihood on held-out validation set.
A.10 Description of Name-Only Classification Baselines.
Glide-Syn (He et al., 2023) This approach takes calegory name as input and employs the word-to- sentence T5 model (pre-trained on CColossal Clean Crawled Corpus' dataset. (Raffel al. 2020) and finetuned on 'CommonGen' dataset (Lin al. 2019)) ), generate diverse concept-speciic sentences. After generating diverse sentences using the word-tt--eentence T5 model, they generate corresponding images using prompts and the Glide (Nichol et. al. 2021 lexi-to-image generative model. Finally, they introduce a clip filter to reduce noise and enhance robustness.
CLIP-ZS (Radford et al., 2021 CLIP-ZS refers to zero-shot classification using pro-trained CLIP model, where the model classifies images without any additional training, leveraging its knowledge from large-scale pre-training. Since CLIP is pre-trained on large-scale web dataset, it demonstrates impressive zero-shot performance QQian et al. 2024).
SuS-X-SD (Udandarao et al., 2023). This approach uses generated SuS (Support Sets) to ensure accurate predictions for target categorics by taking only categorics as input. Specifically, SuS-X-SD generates support sets using Stable Diffusion (Podell et al. 2023) and uses them as a combination with the pre-trained vision language model and an adapter module named TiP-X for inference.
CuPL (Pratt et al., 2023). While the standard zero-shot open vocabulary image classification model, e.g., CLIP [Radford et al. 2021), uses only the set of base prompts, i.e., 'A photo of (category namel', and target images for classification, CuPL proposes to use customized prompts using LLM. Specifically, they propose using GPT-3 (Brown al.j 2020), but we replace with GPT-4o (Wu el al. 2024b), which is a stronger LLM and the one used in our proposed GenOL, for fair comparison.
CALIP (uuo et al. 2023). CALIP enhances the zero-shot performance CLIP (Rafford et al., 2021 by introducing parameter-fi attention module. This module enables visual and textual representations interact and explore cross-modal informative features via attention. As a result, image representations are enriched with textual-aware signals, and text representations guided by visual features, leading to better adaptive zero-shot alignment.
SD-CIf (Li et al., 2023a). SD-CIf leverages large-scale text-to-image diffusion models, such Stable Diffusion (Podell et al. 2023 for classification tasks. Given input and finite set of classes C, the model computes the class- cass-conditional likelihoods po (z]cc. By selecting appropriate prior distribution p(c) and applying Bayes' theorem, SD-CIf predicts class probabilities p(c[ar), effectively classifying the input based on the computed likelihoods.
A.11 Justification for the Use RMD Score
Many recent. works endeavor assess the diversity (Naeem al., 2020 Han et al. 2022: Kim et al. 2024b), complexity (Hwang al. 2023), acsthetics (Somepalli et al. 2024 Khajchabdollahi et al. 2019)] and realism (Chen et al. [2023b] 2024 2023ch of the generated images. In our work, we use the relative Mahalanobis distance (RMD) score (Cui et al. 2023), to evaluate the complexity of the generated samples. The reason for selecting RMD is its independence from the need for real samples in its calculation, while other diversity metries, such as the Rarity Score (Han et al.) 2022) and the TopP&R (Kim et al.) 2024b) require real samples, i.e., data that have not. been generated. Note that our proposed framework, GenOL, operates exclusively with concept inputs rather than real data, thus, we cannot access real data.
As we can see in Figure g the Rarity score and the RMD score exhibit similar trends, showing the ability of the RMD score to effectively measure complexity even in the absence of real samples.
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Dog
Horse
Guitar
Elephant
High RMD. High RS
RMD: 4.4 RS: 49.17
RMD: 4.7 RS: 41.48
RMD: 4.9 RS: 41.14
RMD: 5.2 RS: 40.39
RMD: 4.3 RS: 41.55
RMD: 4.6 RS: 37.09
RMD: 4.4 RS: 37.71
RMD: 4.9 RS: 41.99
Low RMD. Low RS
RMD: 0.3 RS: 28.54
RMD: 1.5 RS: 30.91
RMD: RS: 29.82
RMD: 31.72
RMD: 0.6 RS: 26.9
RMD: RS: 24.67
RMD: RS: 29.16
RMD: 2.1 RS: 26.12
Figure 9: Examples of samples with high RMD & high Rarity scores, as well as samples with low RMD & low Rarity scores. The average RMD scores for Dog, Horse, Guitar, and Elephant are 2.91, 3.03, 2.43, and 3.25, respectively, while the corresponding average Rarity scores are 33.59, 33.58, 33.57, and
33.18.
A.12 Comparison between CONAN and Various RMD-based Ensemble
We compare CONAN with various ensemble approaches that use the RMD score in different strategies on PACS and summarize the results in Table 12 The table revcals that CONAN significantly outperforms others in both ID and Out-OOD domains. Furthermore, with the exception of CONAN, all ensemble methods even exhibit decrease in performance compared to the scenario where ensembling is applied. The k- h-highest RMD ensemble, which excludes easy samples, leads to insufficient learning in the class-representative region, while the k-lowest RMD concentrates solely on casy samples. resulting in limited diversity. Inverse CONAN employs the inverse of the probabilities utilized in CONAN. Similar to the k-lowest RMD ensemble, tends to prioritize easy samples, resulting in limited diversity and accuracy loss. Equal weight ensemble refers to naively selecting outputs generated by each generator at the same ratio, without considering diversity.
ID
OOD
Ensemble Method A
AAUC t
Alast
AAUC t
Alast
No ensembling
51.3642.59
51.63::2449
34.12+1.277
27.18+1.32
Equal weight ensemble
50.5612232
50.03:12113
35.49:11.11
27.13113.14
k-highest RMD ensemble
52.80+2.82
50.09+3.06
36.24+1.52
30.09+1.33
k-lowest RMD ensemble
41.72+2.88
37.98:2..19
31.17+2.34
24.58+2.49
Inverse CONAN
43.01+3.03
38.70+44.12
32.94+1.62
27.61
CONAN (Ours)
55.89-3.066
55.43+2..99
38.53+1.15
33.73+1.82
Table 12:
Comparison of ensemble methods in PACS (hou et al. 20200. using ERR (Rolnick et al. 2019 for
all ensemble methods. The proposed ensemble method outperforms other ensemble methods.
A.13 Details about Metrics
Area Under the Curve of Accuracy (AAUc). In an online CL sctup, the model trained using the stream data in real time, thus, the model needs be used for inference every moment rather than the predefined time point (e.g., end of the task) (Koh et al. 2021 Caccia et al. 2022 Banerjee et al. 2023 Pellegrini et al. 2020). Therefore, to measure inference performance at any time, we evaluated the model at
No ensembling denotes the usage images generated exclusively through SDXL. Podell al. 2023
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regular intervals during specified evaluation period and then calculated the area under the accuracy curve,
denoted AauC (Koh et al. 2021 Caccia et al. 2022 Koh et al. 2023 which is defined as follows:
AAUC
f(iAn)An.
where the step size An is the number of samples encountered between inference queries and f() is the accuracy in the curve of the # of samples-to-accuracy plot. High AAUC indicates that the model maintains good inference performance throughout the entire training process.
Recognizability. Following Boutin et al. (2022) and Fan et al. (2024), we evaluate whether the images accurately represent the intended concepts by computing the Fl score for each class, We pre-train ViT-Base from scratch with MA data and use classify the images generated from each prompt diversification baseline. Recognizability is then calculated by averaging the F1 scores across all classes.
Diversity. To assess the diversity of generated images, Naeem et al. 2020 measures coverage, defined as the ratio of real samples encompassed by the generated samples. Specifically, they calculate the fraction of real samples whose k-nearest neighborhoods contain at least one generated sample. Formally, given the embedded real and generated data, represented by {X;} and (Y₃} from an ImageNet pre-trained ViT-Base feature extractor, coverage defined as:
N
coverage
N 13i (XD.)
1
Σ
where NND&(X.) denotes the distance from X, to the 4tth nearest neighboring among {X;) excluding itself and B(x,r) r) denotes the sphere in TR' around x with radius r.
consensus-based Image Description Evaluation (CIDEr). In the MVCIL-CA setups, compare podel-predicted sentences with ground-truth sentences, sures the similarity between generated and ground-truth sentences while also capturing aspects such grammaticality, saliency, importance, and both precision and recall. CIDEr (Vedantam et al. 2015) aims to automatically evaluate how well predicted sentence, Sp, matches the consensus of a set of ground truth sentenecs, S = ($gt,1.....,$g) $gt.v.. The intuition is that the measure consensus should encode how often n-grams from the candidate sentence appear in the reference sentences. In contrast, 7-grams that are absent from the reference sentences should not appear in the candidate sentence. To encode this intuition, they caleulate the TF-IDF (Robertson, 2004) vectors for the n-gram elements within the candidate and refer- ence sentences by computing the cosine similarity between the two vectors. Formally, CIDEr for n-grams is calculated as follows:
CiDER (Vedantam et al. 2015). which mea-
CIDEr sgr) = (sppp) (sge)
(9)
llg (1l9))
where g(s) represents the vectorized form of sentence s, obtained by calculating the TF-IDF values for its n-gram clements. Finally, they combine the scores from n-grams of varying lengths as follows:
N CIDER
Σ CIDER
(10)
N
Following Vedantam et al. 2015 we use N = 4 and define the set of ground truth sentences in the positive set as S.
A.14 Details about Generators
For the set of generators G, we use five text-to-image generative models: SDXL Podell et al. 2023). Deep-
Floyd 1 SD3 [Esser et. al. 2024
Viewiew2 (Ding et. al. 2022 and Auraflow As illustrated in Figure
https://github.o http:///ssccubbcccc//eeee/o/////
http:///uggiggccc.o/////////rr
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10 different generators produce varied samples when prompted with identical prompts conditioned the same concept. Details of each generator are as follows:
SDXL. SDXL is an enhanced latent diffusion model for text to image synthesis, building upon the previous versions of Stable Diffusion. Specifically, SDXL introduces three key improvements: (1) a UNet (Ronneberger ict al. 2015 backbone that is 3x larger than in provious Stable Diffusion models, (2) an additional condition- ing lechnique, and (3) diffusion-based refinement model to further improve the visual quality of generated
images.
DeepFloyd IF. DeepFloyd IF utilizes frozen text encoder alongside three cascaded pixel diffusion stages, Initially, the base model produces 64x64 image from text prompt, which is then progressively enhanced by two super-resolution models to reach 256x256 and ultimately 1024x1024 pixels. At every stage, the model uses frozen T5 transformer-basee text encoder to derive text embeddings, which are then passed into a UNet.
CogView2. CogView2 pretrain a 6B-parameter Iransformer using a straightforward and adaptable self- supervised task, resulting in a cross-mas cross-modal general language model (CogLM). This model then fine-tuned for efficient super-resolution tasks. The hierarchical generation process is composed three steps: (1) A batch of low-resolution images (20 20 tokens) first generated using the pretrained CogLM. Optionally, poor-quality samples be filtered out. based the perplexity of CogLM image captioning, following the post-selection method introduced in CogView (Ding al. 2021p. (2) These generated images then upscaled to 60 60-token images via a direct super-rosolution module fine-tuned from the pretrained CogLM. (3) Finally, these resoluinnn images are refined through another iterative super- reper-resolution module fine- tuned from CogLM.
SD3. SD3 enhances current noise sampling methods for training rectified flow models (Liu et al. 2023d by steering them toward perceptually significant scales. In addition, SD3 introduces a new transformer- based architecture for text-to-image generation, employing distinct weights for the two modalities. This design facilitates bidirectional flow of information between image and text tokens, leading to improved typography, text comprehension, and higher human preference ratings.
AuraFlow. AuraFlow, inspired by SD3, is currently the largest pext-to-image generation model. It intro- duces several modifications to SD3, including the removal of most MMDiT blocks (Esser et al. 2024 and their replacement with larger DiT encoder blocks (Peebles & Xie 2023
A black and white image of [person] highlighting dramatic contrasts.
photo of [person] in earth tones.
Prompts
SDXL
DeepFloyd IF
CogView-2
AuraFlow
SD3
Figure 10:
Examples of PACS Zhou et al. 2020 generated samples from various generators using two of the prompt rewrites. Illustrations from the concept. 'Person' are showcased.
A.15 Extended Related Work
Continual learning setups. Many recent works propose realistic CL scenarios, such as blurry (Prabhu et al. 2020 Bang et al. 2021), i-blurry (Koh et al. 2021),, continuous (Shanahan et al., 2021 Koh et al. 2023), and noisy (Bang
2022) sctups. However, they only focus on the data distribution of the stream data, rather than the acquistiion of data for new concepts, for which the model needs be learned. Recently, C2C (Prabhu et al. 2024) used web-scraped data for continual learning, to address the high cost of manual
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data annotation and the difficulty in acquiring real-time data for the target concepts the model needs to learn. However, web-scraped data suffer from noise, low controllability, and usage restrictions, as highlighted in Figure Please refer to Section A.30 for more detailed explanation of the web-scraped data.
Methods for Continual Learning. Replay-based method, which stores data from previous tasks in episodic memory for replay, one of the most widely used approaches, due to its effectiveness in preventing catastrophic forgetting (Zhang et al. 2023b Yoo al. 2024 Kozal et al. 2024). However, despite their effectiveness in preventing forgetting, they raise data privacy concerns due to the storage of real data from previous tasks in episodic memory. To address these privacy concerns, pseudo-replay approaches have been
proposed Graffieti et al. 2023 Thandiackal et al. 2021 Van de Ven et al. 2020 Shin et al. 2017 Van de
Ven & Tolias 2018), which leverage generative models to generate images of previous tasks instead of storing actual data in episodic memory. While these approaches utilize generative models similar to our GenOL
framework, they still require manually annotated data to train the generative model (Shin et al. 2017 Van de
Ven ct al., 2020 Van de Ven Tolias 2018). In contrast, our GenOL framework climinates the need for any manually annotated data, relying solely the category names the model aims learn.
A.16 Quantitative Analysis on CIFAR-10-W
We compared GenOL and generative baselines combined with CONAN, for a fair comparison on CIFAR- 10-W (Sun et al. 2024). As mentioned in Section A.2, since CIFAR-10-W only contains data on the OOD domains of CIFAR-10, we evaluated only the performance of the oustof-iistt (OOD) domain.
In addition, since CIFAR-10-W is a web-scraped dataset, the domain of CIFAR-10-W and the web-scraped data the same. Therefore, we excluded web-scrapping baselines in experiments CIFAR-10-W. We summarize the results in Table B
CIFAR-10-W
Method
AAUC f
Alost f
Glide-Syn (ICLR. 2023)
47.1410.80
34.13:10.54
LE (ICLR 2023)
47.20
34.03:+0.60
(+) CONAN
51.69:10.70
41.324:..8
CHB (CVPR 2023)
45.30
31.20:10.54
(+) CONAN
51.29:10.72
37.06:11.77
SC (CVPR 2024)
44.75.10.62
30.41110.84
(+) CONAN
48.60+10.33
36.39+0.88
CCG (CVPRW 2024)
38.96:0.941
24.71110.70
(+) CONAN
41.3240.99
28.9440.77
HIRPG
52.52:0.37
41.0411.26
(+) CONAN (Ours)
55.5310.41
43.51
Table 13: Quantitative comparison with diverse prompt generation basclines CIFAR-10-W.
A.17 Pseudocode for the GenOL
Algorithm I provides a detailed pseudocode for GenOL. When a new concept is encountered, the prompt generation module vi generates concept-specific prompts. These prompts are then used by a set of T2I generators G create concept-specific images. Subsoquently, the ensembler A selects coresct from these generated images for efficiency, instead of training on the entire dataset. The continual learner is then trained using this selected ensemble set. During training, GenOT. also stores small portion of previously generated
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Algorithm GenOL
1: Input Model fa, Prompt generation module v,, Set of Generators G, Ensembler A, Concept stream C, Learning rate μ, Episodic memory M
2: for y E y do
b New concept arrives from concept stream y > Generate prompt set Pe for given concept using di Generate image set X. using 9 and P. > Ensemble generated image set using ensembler > Calculate cross entropy loss Update model Update episodic memory
3: Generate Pe + (b() Gl
Generate {Xe li=1 f- ((P))
5: (dc,c) + A
(1 1i=l)
L=
Update 8 8 p. VoC
Update M ReservoirSampler (M, (Xas c))
end for 10: Output fo
Algorithm 2 RPG
1: Input Maximum number of leaf nodes of K-ary Tree K, System prompt Ps, Large language model LLM, Prompt of parent node Pa.
2: P+0 e
P Initialize the generated prompt set p Initialize number of child node Pa, Generate Kg child node of Pa.k
while k < K do
if k' ===1 then
Pa+LE + Pa,
7: else
8:
PA+I.E + LLM((,,, Pa.k UP)
b Recurrently forward the previously generated prompts P Add the currently generated prompts to P
9:
end if
10: P+ PU
11: W+
12: end while
13: Output P
Algorithm 3 HIRPG
1: Input Newly encountered concept Maximum number of leaf nodes of K-ary Tree K, Prompt of parent
node Pa.k
2: P - RPG( (a.x)
Generate K number of prompts using RPG > Initialize the prompt set generated from the child nodes
3: Pch 0 0
if d then
for Pkr E P do
Par Per (
Merge prompt generated in child noes
end for
8: end if
9: Output PUPe
b Merge the prompts generated in the child nodes and the current node, then return
samples in episodie memory M. Although GenOL can generate images related to previously encountered concepts, retaining these samples helps to reduce computational overhead.
Additionally, Algorithm 3 Algorithm 4 and Algorithm 5 provide a detailed pscudo code for prompt gener- ation module eh, set of generators G, ensembler A, respectively, which are components of GenOL.
A.18
Details about Web-Scrapping
For web-scrapping, we follow C2C (Prabhu al. 2024), which proposes scraping data from the web using category names. C2C (Prabhu al. 2024h uses four search engines, including Flickr, Google, Bing, and
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Algorithm 4 Set of Generators G
Input Rewritten prompt set P, Generative models = {91 ..., 9|0 > Initialize the sets of generated images for each model
2: Ul....,Ue U1g < 0....,
3: for p P do
4: for 9i EG do
5:
By (:()) U, t (1 (0)
Generate (6)
Generate image zy (9) using prompt p and generative model
b Append z!{) to the set Up
end for end for Output {Ui, U2 UiSit
Return the generated image sets for each model
Algorithm 5 Ensembler A
Input Generated image sets {U1, U2. Upoi Coreset size [V], Temperature parameter T
2: U + - U3
Combine all generated image sets into a single set U
3: for each sample (zi, v1) E U do Compute RMD(x;; vi) + M(xi, - Magn(
Compute RMD scores for each sample
end for Truncate R.MD(I,, vi) Normalize RMD (24, t RMD(I V6)
Remove outliers from RMD scores > Apply Z-score normalization
8: Compute Pxly = RMDZIN/"
for each x E
Compute the selection probability using softmax
Σxeu e RMO.Y /
RMD
function
9: Select V + Sample [V] images from U based on probabilities Pely
10: Output Coreset V
Dog
Elephant
Giraffe
Guitar
Horse
House
Person
THE EDERLAAN
BUTTARA
OPEN HOUSE
Figure 11: Examples of noisy raw data obtained via web-scraping for the classes in the PACS dataset.
DuckDuckGo, using the publicly available querying too collect URLs. While C2C uses four scarch engines for scraping, we only use three search engines, i.e., Flickr, Google, and Bing, since ICrawler did not support web data scraping from DuckDuckGo at the time of our attempt February 20, 2024. After collecting the URLs from each search engine, we use a multi-throaded downloade to quickly download the images, following (Prabhu et al. 2024). For Flickr, we are able to download approximately 500 images per minute due to the rapid URL collection facilitated by the official API. Meanwhile, for Google and Bing, the download rate is slower, at approximately 100 images per minute on a single CPU machine. However, the download rate depends on the network conditions and the status of the API and the search engine. In C2C, the ensemble of web-scrapped data from search engines is weighted differently for each benchmark. For example, in the Stanford Cars benchmark, the weights Google: Bing: Flickr 5:4:2, while in the Flowers benchmark, they are 1:1:2, respectively. Since use different benchmarks compared to C2C, we
httpee///sttubb.cm///lll//////// https://github..cmm//ee http://hhhh..aoooooooo/o/////aaa
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select equal weight selection to ensemble web-scrapped data, i.e., Google: Bing: Flickr 1:1:1, which is one of the most soraig and widely used ensemble techniques (Shahhosseini et al. 2022 Ju et al. 2018).
Datasets scraped from search engines such as Flickr, Google, and Bing contain uncurated (i.e., noisy) samples. To clean these datasets, following (Schuhmann et al., 2022 we use a pre-trained CLIP (Radford et al. 2021 model to measure the similarity between the images and corresponding promts. Specifically, we scraped 10% more data than the required dataset size (i.e., the number of manually annotated data) and removed samples with low CLIP similarity score for each experiment. Although prior work (Prabhu et al,, 2024) addressed the ambiguity of queries through manual query design, such as adding an auxiliary suffix to refine queries, in an online CL scenario, where new concepts stream in real-time, such hand-crafted query designs for each concept are limited.
In summary, data noise, network dependency, and the need for manual query design specific to each concept restrict the use of web-scrapped data in real-world scenarios where new concepts are encounterod real-time.
A.19 Comparison of CONAN with Hard Negative Sampling Methods
In addition to comparing CONAN with coreset selection methods, we also evaluate it against hard negative sampling hods which prioritize hard samples based on their own selection criteria, including HCL. Robin- son et al. 2021 and H-SCL (Jiang et al. 2024). We summarize the results in Table 14
As shown in Table 14 CONAN outperforms hard negative sampling baselines in both PACS and DomainNet. We believe that the lower performance of hard negative sampling methods stems from the lack of class- representative samples, which are crucial in coreset, similar to the lower performance with k- k-highest RMD selection strategy in Section A.12
PACS
DomainNet
Method
ID
OOD
ID
0OD
Axco f
Atoo
AAUC
Anex 1
AAUC f
Aba
Axcc
Atn f
HCL
51.18:22.76
46.10-3.77
36.52:1.66
29.25::2.22
30.34+0.78
2..76::0.82
13.26:0.56
11.56=0.43
SCS
1..55:+1.69
47.41:2.91
1.29
27.57+11.00
32.22+0.34
26.47:+0.25
13.88+0.18
11.36=0.20
CONAN (Ours)
55.89+33.06
55.43422.49
38.53+1.15
33.73:11.82
34.60=0.31
30.09:0.11
14.53+0.22
12.65+0..09
Table 14: Quantitative comparison between hard negative sampling basclines on CIL sctup.
CONAN outperforms hard negative sampling baselines in both ID and OOD domains.
A.20 Additional Analysis of Bias
We summarize the attribution keywords used for analysis of bias in Tab. 15
Effect of Bias To analyze the impact of biased data in contin- ual learning, we continually fine-tune CLIP model on the PACS dataset acquired by baselines and GenOT,, which includes the person class. We then evaluate whether the predictions for the person class exhibit biases toward specific genders. Specifically, we fine-tune a pre-trained CLIP model on the person class using the prompt 'A photo of a person' and person images generated by each method. If the training data acquired by a method is biased toward specific gender, the model will likely make biased predictions for that gender in the test set. During evaluation, we measure accuracy, which eval- nates whether the model correctly predicts the ground truth gender of the test data, and we summarize the results in Table 16 As shown in the table, CLIP models trained on data generated by CHB and CCG, which exhibit significant biases toward specific genders in Figure 5 tend to refloct these biases in their predictions. In contrast, CLIP models trained using data generated by GenOL correctly predict gender with higher accuracy (ie., less bias), highlighting the unbiased and diverse data generated by GenOL.
Method
Accuracy
C2C
71.26
CHB
64.37
CCG
64.72
SC
72.16
LE
74.71
GenOL (Ours)
77.01
Accuracy of the fine- tuned CLIP model on the 'per- son' class in the PACS datasct.
Table 16:
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Bias Type
Category
Attribution Keywords
Female
('she', 'her", 'hers', 'woman', 'female' 'gárl"
Gender
Male
['kee, 'him', 'his', 'man', "male", 'boy"]
Black
Blaack person', 'Black man; 'Black womon', 'Block boy'; 'Black gärl") (White person',
Race
White
'White man', White woman', 'White boy', 'Whste girl'] 'Asion woman', 'Asian boy', 'Asion girl']
Asian
Asian person', 'Asian man',
Table 15: Attention keywords for Gender Race bias. We categorize gender bias into female and male and race bias into Black, White, and Asian, respectively.
Geographical Bias In addition to gender/race bias, we measure the geographical bias objects, evalu- ating whether the generated content reflects artifacts and surroundings from across the globe, rather than disproportionately representing certain regions, proposed by [asu al. (2023). Specifically, similar to the method for measuring gender and race bias, calculate the similarity between the text embedding of 'a high-definition image of typical {concept) in (nation)' and the image embeddings for all concepts in the dataset. We summarize the results in Figure 12 As we can see in the figure, data generated by GcnOL does not produce data biased toward specific regions but instead generates data from various regions for the same concept, even when compared to MA.
MA
C2C(Web-scraped)
LE
GenOL
4.4%
4.2%
15.5%
12.3%
11.4% 13.1%
25.8%
18.0%
25.1%
19.1%
14.3%
17.5%
15.1%
17.8%
7.9%
15.2%
9.6%
17.4%
7.0%
19.9%
10.9%
12.8%
9.0%
10.7%
20.6%
13.6%
19.4%
USA UK
South Africa Canada
France China
South Korea
Figure 12: Comparison of geographical bias between GenOL, web-scraped data (i.e., C2C), and manually annotated (MA) data. Compared to C2C and MA, GenOL and LE generate data that is evenly distributed across various geographical regions.
A.21 Quantitative Analysis on Fine -iniigrineed Benchmarks
We evaluate GenOL on fine-grained benchmarks. Specifically, as our focus extends beyond the accuracy of the ID domain include that of the OOD domain, conduct experiments Birds-31 (Yu et al. 2024 fine-grained domain generalization benchmark comprising CUB-200-2011 (He & Peng 2019 NABirds (Homn
et al. 2015) and INaturalist2017 (Van Horn et al. 2018). We summarize the results in Table 17
As shown in the table, GenOL outperforms the baselines and achieves performance comparable to MA. We attribute this success to GenOL's ability to effectively generate diverse images of tne-grained species through our proposed prompt diversification method (i.e., HIRPG) and data ensembling method (É.e., CONAN), as shown in Table g and Table 4
ID
OOD
Method
AAUG
Atast 1
AAUG
A 1
MA GenOL (Ours)
26.19:1.12 24.25+0.41
16.0941.97 20.06-0.93
21.2910.76 18.6240.33
12.98+0.74 13.49+1.01
Table 17: Qualitative comparison of baselines Birds-31. GenOL shows performance comparable to manually annotated (MA) data.
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A.22 Comparison of concept-specific prompts and concept-agnostic prompts
We can categorize prompt diversification strategies into two categories: and concept-aware diversification. Concept-agnostic diversification strategy first generates a set of diverse prompls, e.g,, {A vibrant photo of {concept) during sunrise with a warm color palette, A cinematic wide- angle shot of {concept] dusk, ...}, and then inserts given concept into the concept placeholder. As result, prompts for all concepts follow the same sample templates. In contrast, concept-aware prompts generate unique prompts for each concept, which can differ in aspects like background and color schemes.
concept-agnostic diversification
LE, SC, and CCG generate concept-aware prompts, which cause prompt generation time to scale with the total number of classes, as revising prompts using LLMs, such as GPT-3.5, can enhance their naturalness by incorporating suitable backgrounds and styles for each class. contrast, GenOL employs the same template across all classes and benchmarks, inspired by the renowned psychologist Dr. K. Anders Friesson, who argued that high-end performance of human results from extensive practice beyond one's comfort zone Ericsson et al.) [1933); Huang et al. (0022). In other words, we believe that samples generated from such unnatural prompts can facilitate the learning of concepts from more diverse viewpoints, thereby enhancing generalization ability, To this end, by applying fixed templates across all concepts rather than generating concept-specific prompts, we can significantly reduce computational costs i.e., prompt generation time becomes independent of the total number of concepts- whill also providing opportunities train with more challenging samples.
A.23 Effect of Hyperparameters
Hyperparameters, including temperature т, trun- cate ratio L, depth D and width K of K-ary tree, are selected through hyperparameler search on DomainNet and are consistently applied to the other benchmarks.
Effects of K and D (100 Prompts)
0.34
D=1, K=100
D=2, K=10
0.32
D=3, K=5
Diversity
0.3
D=4, K=3
Effect of Depth D and Width K Our pro- posed prompt diversification method (i.e., HIRPG) utilizes a hicrarchical tree structure. Specifically, we construct a complete K-ary tree with depth of D, allowing us to generate K K-1 diverse prompts. To generate desired number of diverse prompts, we can adjust the parameters by either increasing and docrcasing D, or decrcasing K and increas- ing D. Below, we demonstrate the effects of the tree's depth (D) and width (K), and explain how we selected the hyperparameters used in HIRPG.
0.28
0.26
65 Recognizability Figure 13: Effect of K and D of K-ary tree in HIRPG. We generate 100 different prompts using var- ious combinations of K and D of the Iree. We then use these prompts to generate images and measure the Rec- ognizability and Diversity of the generated images.
0.24 60
70
75
In Figure 13 and Figure 14 the case of D demonstrates low diversity and recognizability. This occurs because significantly increasing K and decreasing D can lead to difficulties in fully utilizing information within the long context, a challenge referred to as the mosd-ln-theemiddlle problem (Liu et al. 2023c An et al., 2024). Specifically, generate N distinct prompts, we itcratively generate prompts at cach RPG step. In the final step, N previously generated prompts are used as negative examples. Providing such a long context. to the LLM can hinder its ability to fully comprehend and effectively reference the negative examples (í.e., previously generated prompts). As result, the LLM may produce unrocognizable prompts or generate prompts that duplicate previously created ones.
The cases of D = 3 and D = 4, which correspond to low K values, exhibit low recognizability. This is because providing only a few negative examples offers insufficient context. Specifically, recent studics (Agarwal et al. 2024) have observed significant performance improvements across various tasks when using many-shot examples, compared to few-shot examples, in in-context. learning.
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Effects of K and D (50 Prompts)
Effects of K and D (50 Prompts)
0.34
D=1, K=50
0.35
D=1, K=50
D=2, K=7
D=2, K=7
0.32
0.33
D=3, K=4
D=3, K=4
Diversity
0.3
D=4, K=2
Diversity
0.31
D=4, K=2
0.29
0.28
0.27
0.26
0.25
04
0.23
70 Recognizability
85
Recognizability
(a) Generating 50 prompts PACS
(b) Generating 50 prompts in NemainNet
Figure 14: Effect of K and D of K-ary tree in HIRPG. To generate 50 different prompts PACS and DomainNet, we generate prompts using various combinations of K and D of the tree. We then use these prompts to generate images and measure the Recognizability and Diversity of the generated images.
By balancing the trade-offs of both scenarios, recommend selecting appropriate values for K that avoid being excessively high or low, along with the corresponding D. In our experiments, we use D = 4 and K = 7 to generate 50 distinct prompts.
A lower temperature causes the ensemble method to focus more on samples with high RMD scores (i.e., difficult samples). However, sctting the temperature too low can hinder per- formance by excluding low RMD samples (i.e., easy samples). Conversely, a higher lemperature increases the likelihood of including samples with low RMD scores (i.e., easy samples), but setting it too high results in an ensemble containing too many casy samples. Therefore, we select an appropriate temperature via a hyperparameter search. The results of this search are presented in Figure 15 While both excessively high and low temperatures lead to diminished performance, there is wide range of temperatures that maintain stable performance.
Effect of Temperature
Effect of Temperature
37
31
AAUC(ID)
Alasc(ID)
35
30
33
29
31
28
2
23
2
2
Temperature T
Temperature T
16
13
(AUc(OOD)
Alot)OOOD)
15
12
14
11
13
12
10
1
Temperature
Temperature T
Figure 15: Effect of temperature T. We mcasure AAUG and Atast both ID and OOD domains in Nel.inNet.
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Effect of Truncate Ratio L. In CONAN, truncate the samples with RMD scores in the upper and lower 1% to minimize the impact of outliers the probability distribution. Truncating very small portion of the candidate set may cause the ensemble set to include outliers and generated images with artifacts. In contrast, truncating a large portion of the candidate set can discard difficult samples that are not outliers, as well as easy samples (i.e., conceplireereesnnlttive samples) that are crucial for coreset construction (Bang et al. p.021. Therefore, as shown in Figure 16 both very high and low truncation ratios cause performance degradation. To this end, we select an appropriate truncation ratio by balancing the advantages and drawbacks of high and low truncate ratios.
Effect of Truncate Ratio
Effect of Truncate Ratio
36.0
31.0
A
Alas((ID)
35.0
30.0
34.0
29.0
33.0
1.25
2.5 3.75
6.25
15
25
35
28.0
1.25
2.5
3.75
6.25
<15 15
25
35
Truncate Ratio L
Truncate Ratio L
Effect of Truncate Ratio
Effect of Truncate Ratio
16.0
14.0
AAuc(OOD)
Alast(OOD)
15.0
13.0
14.0
12.0
13.0
2.5 3.75 6,25 15
35
11.0
1.25
2.5
3.75
6.25 <<
35
Truncate Ratio L
Truncate Ratio
Figure 16: Effect of Truncate Ratio L. We adjust the truncation ratio over a wide range DomainNet and measure A AAC and Alast on both in-distribution and out-of-listribution domains.
A.24 Quantitative comparison of name-only baselines on recent prompt- CL methods.
In all data acquisition baselines, including our proposed GenOL, we consistently employ ER as the CL method, widely adopted and strong Cl. strategy (Seo et al. 2025 to ensure fair and consistent compar- ison. Beyond this, also compare GenOL with other data acquisition basclines recent prompt-base based and eeffiieter-ffficeen CL methods (i.e., MISA (Kang el al. 2025 and C-Prompt (Gao el al. 2024) validating its applicability regardless of the underlying CL algorithm. We summarize the results for MISA and C-Prompt in Tab. [8 and Tab. 19 respectively. As shown, continual lcarning on data acquired by GenOL consistently outperforms baselines, highlighting that GenOL can be effectively combined with future state-of-the-art continual learning algorithms.
A.25 Quantitative analysis using Qwen and LLaMA LLMs
The LLMs used in both the generative basclines and our proposed GenOL can be readily replaced with different LLM architectures. To verily whether GenOL consistently outperforms the baselines across various LLMs, we compared its performance when using Qwen and LLaMA as the underlying LLMs, in addition to the GPT-4o results reported Tab. Specifically, we employ LLaMA-3-8B and Qwen-3-8B and summarize the results in Tab. 20 and Tab. 21 respectively. As shown, GenOL consistently surpasses the basclines regardless of the underlying LLM,, although the absolute performance varies across models. These findings
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PACS
DomainNet
Method
ID
OOD
ID
OOD
AaUC
Aloxt
Axuo
Aloxt
AAUC 1
Alast f
AAUC
Atont
LE
72.08+2.32
24.70:+2.31
7..7777775
32.79+1100 68
29.27+1.54
22.51+2.37
9.90=1.07
7.4222144
(+) CONAN
76.76=1.82
57 57.666:11.93
51.88=2.25
36.13:00992
37.13:2.12
32.1
13.2242.03
11.37#1.26
CHB
70.02+2.28
62 0:222.228
1.771=1776
22.11+1554
41.022+2.44
30. 30.19:2299
4.60=1221
11.74#2.28
(+) CONAN
67.48:11.10
48.86-4-2.02
43.53
27.07 1.83
52.42.12.12
4..4
19.394 1.08
16:29:11.68
64.4311.96
53.511:1.38
34.01:1.66
21.28:1:.00
46.49:11002
38.42:1.28
16.92:12216
12.90
(+) CONAN
66.68111.65
50.35:1-2.66
38.50+2.004
27.04
52.23:12.65
44.2811191
17.804:2.06
1..6
CCG
59.85.12.39
62.72:111.73
33.97:1.68
23.
23.00:1:2.33
30.17:12.74
23.8611.52
10.52:1167
7.55+1
(+) CONAN
63.27+2.16
53.02:+1.22
40.81=1.98
188
1..17=1.21
38.72+2.13
16. 1.778
13.33#2.10
HIRGG
76.75++2.62
21.33
44.71=2.11
336 36.40-+2336
2..70+22.2
44.60+22.29
21.85++2.05
18.12+0.95
(+) CONAN (Ours)
79.89=2.62
69.14+1.88
54.99=00.67
39.82=1.50
63.48=22.05
16.17=2.33
24.66=0..7
20.05=+0.98
MA
87.92++1.85
81.80++1.74
40.30+
28.06-+-2.22
73.78+22.03
68. 68.74+11005
25.55++0.95
22.85+1.88 1.88
Table 18: Quantitative comparison of name-only baselines using MISA for continual learning.
MA refers to training a model with manually annotated data. The combination of HIRPG and CONAN
refers to our proposed GenOL. We bolded the highest accuracy, excluding the MA data (í.e., ideal scenario).
PACS
DomainNes
Method
ID
OOD
ID
OOD
Aaui
Absa
Axso
Alext f
AxtC f
Ainse f
AAUC 1
Atoxt ?
LE
73.80+1.98
69.49:::.01
43.75=1.40
36.08+1994
33.74+2.41
23.59+1.72
10.35:+992
8.30+22.12
(+) CONAN
76.03
70.87:42.10
3..20
44.13411.11
41.44:11.9
30.88
13.60:1152
10.11 4.209
CHB
71.63:11.7
65.92:1:..5
34.33:1.89
28.62:11.88
28.84:12.12
23.0811.97
9.36=2228
5.55:111.88 884
(+) CONAN
68.60:2..1
53.40:111.79
43.60:2.02
37.81 1-188
42.35:12.2
28.02
12.45:12..88
9.224-2.10
SC
59.02:1195
51.47:1:.19
28. 1.119
20.14111.0
21.43:2,23
10.11:1.22
6.89
3.45:11.99
(+) CONAN
67.81:12.46
5..10:11.24
18.26:11.70
25.62 24
1.447
27.46:12.67
1.84
6.871:2.03
CCG
11.73:2.18
46.22442.57
29.09=1.76
2..5411995
19.27:1..10
13:2911.44
5.77/4:2.00
4.43:12.11
(+) CONAN
61.98+2.22
50.09+11.09
43. 43.29=2.10 19
31.67+1.53
22.80+1124
12.75+1..5
7.96=2.31
6. 6.60+22112
HIRPG
1..8
64.73++1.68
44.25+2.011
34.91+1.86
37.80+22.24
25.104
16.1241..63
11.31
(+) CONAN (Ours)
77. 17.62+1.55
72.96=1.11
54.24+1..99
44.38=1.03
41.17#2-42
31.41=2.59
18.19:1.65
15.11=1.84
MA
88.40+11.85
85.99+
30.56+2.76
30 30.46++2.55
61.59+ 1.89
50.83++2.20
20.47++130
15.02+1.37
Quantitative comparison of namc-only basclines using C-Prompt for continual learn- ing. MA refers to training model with manually annotated data. The combination of HIRPG and CONAN refers to our proposed GenOL. We bolded the highest accuracy, excluding the MA data (í.e., ideal scenario).
Table 19:
PACS
DonninNes
Method
OOD
ID
OOD
AauC f
Aboxt
Axvo f
Atext 1
AAUC 1
Atase f
AxUC 1
Atent
CHB
48.02+1.80
45. 15.6443.19
29.30=1.40
20 20.65550.55
14.71
12.13+1108
2.59=1.13
2.35+11766
(+) CONAN
49.35
47.124:2.22
30.67
23.0641..25
28.41:40.9
23.81
0.96=0.19
7.01-10.18
SC
48.44122.50
45.5412237
30.55:1.35
23.37:1044
16.88:10.28
12.7610.26
5.05=0018
3.70:10.10
(+) CONAN
4..49
26.95:12.94
3.53
1..8
22.43:12.08
11.51
9.17
8.61
CCG
45.35:22.76
43.46::1.76
31.06:1.600
24.08:10.72
16.40:10.20 20
13.03:1029
0.89=0,12
3.59:10.03
(+) CONAN
48.71
47.10:12.13
1..41:1.46
25.08 1.060
17.98.11.98
16.69
8.05
5.96:1-2.00
HIRPG
47.36++2.62
44.314-2.00
33.62+1.52
26.23++3.18
18.86+22.14
13:90+22.08
6.81=0.94
5.54++2.00
(+) CONAN (Ours)
19.71=2.40
49.1441.78
35.56+11.50
31.68=1.91
29.53++.23
24.58=0.26
15. 15.79700227
13.22-+0.03
MA
67.10:44007
61.95++0.92
27.75=1.44
20.90+-0.95
51.13+0..2
42.95-40.15
13 13-48-40.09
10.69+0.07
Quantitative comparison of name-only baselines using LLaMA3-SB as the LLM. MA refers to training a model with manually annotated data. The combination of HIRPG and CONAN refers to our proposed GenOL. We bolded the highest accuracy, excluding the MA data (i.e., ideal scenario).
Table 20:
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PACS
DonminNes
Method
ID
OOD
ID
OOD
AAue 1
Alout
AAUO f
Aloxt 1
AAUC 1
Alast f
AAUC 1
Alow 0
CHB
45.29+11660
37 272277332
29.89=0.955
20.1++111
17:33+0.11
13.55=00.9
5.83=00.66
4.06++0.20
(+) CONAN
48.29
2..6441..85
31.32-10.58
25.141:0.51
30.46:1.111
2..65
12.42:10.6
6.31
SC
47.26122.6
45.70:11.14
30.62-1.677
22.72:1.11
12.47:0.27
10.15:10:26
4.11=0.24
3.13::0.13
(+) CONAN
50.02:12.66
49.97:42.09
31.02-2.001
23.29
21.11:10299
18.881:0.99
6.14:0.12
5.25:10.03
CCG
27.55
45, 1.76
31.06:1160
24.08110.22
16.98:10.27 27
15.15:10.26
5.21=0.09
4.30:10.07
(+) CONAN
48.18:22.5
19.45:11.17
1.77
25.03:10.90
16.98:10.27
15.15:10.26
5.21:00099
4.30.10.07
HIRPG
48.57422.40
1..73+1167
33.61=1.78
17.34++.110
20.64++0611
16.68+0.38
8.71=0.26
6.83++0100
(4) CONAN (Ours)
50.89=1.94
49.54+2.13
34.77+1007
28.89=0.78
30.56++1.37
26.78=0.56
14.93+1.80
10.68=0.87
MA
67.10+44.07
61.95++0.92
27.76
20.90++0.95
51.13++0.28
42.95++0.15
13.48-+0.00
10.69+0.07
Quantitative comparison of name-only baselines using Qwen3-8B as the LLM. MA refers to training a model with manually annotated data. The combination of HIRPG and CONAN refers to our proposed GenOL. We bolded the highest accuracy, excluding the MA data (i.e. ideal scenario).
Table 21:
suggest that as stronger LLMs become available, GenOL will be able to take advantage of them to achieve even greater performance.
A.26
Comparison of CONAN with baselines using few-shot real samples
In real-world applications, beyond the name-only setup, several practical examples can be integrated with the concept. Accordingly, we extend our evaluation by comparing the proposed GenOL with few-shot baselines (c.g.. DreamCache (Aiello et al. 2025), DrcamBooth (Ruiz et al.] 2023), Bootpig (Purushwalkam et al. 2024 that employ few-shot real samples for generation and summarize the results in Fig, 17 Since few- shot baselines fine-tune generative models using real samples, we also fine-tune the generative models in GenOL using LoRA, following Ruiz et al. 2023), for a fair comparison. Specifically, since GenOL employs multiple generators while few-shot generative basclines use a single generator, we compare the baselines with HIRPG, which excludes the ensemble method (fe., CONAN) in GenOI. and only uses SDXL, (Podell et al. 2023). Here, SDXL is fine-tuned with real data. We refer to HIRPG with fine-tuned SDXL as HIRPG-FT. Additionally, for reference, we present the results for the scenario where no real samples are used (i.e., assuming zero-shot real samples). In this case, refer to the baseline performance of HIRPG and GenOL as HIRPG-7S and GenOI.77,, respectively. As shown in the figure, HIRPG-FT outperforms the baselines in both ID and OOD domains on both PACS and Net. This demonstrates that when HIRGG is combined with real sample fine-tuned SDXL, it generates data that is both diverse and highly recognizable, drawing from real data, as a reference. Notably, increasing the number of real samples significantly improves ID accuracy; however, for OOD performance, accuracy converges after approximately three examples, with some baselines even showing a slight degradation as more real samples are added. This occurs because fine-tuning generative models leads to image generation that closely matches the style and background of the provided real examples, thereby enhancing ID accuracy. However, this simultancously reduces dataset diversity and limits OOD generalization.
A.27 Qualitative Results for Prompt Generation Methods
We also qualitatively evaluate the performance of our proposed prompt generation method, HTRPG, against existing prompt diversification basclines, including LE, CHB, SC, and CCG. The comparison illustrated across multiple concepts from the PACS and DomainNet datasets, as shown in Table 22 Table 23 and Table 24 We observe that most methods are not able to generate diverse prompts well maintain coherence and logic across generated instances. Common issues across bascline methods include irrelevant content, repetitions, and overused phrases.
LE generates repetitive phrases across difference concepts that, while slightly different in wording, essentially convey the same meaning. In Table 22 despite that phrases differ in their choice of words, they describe the same visual concept: a subject illuminated by soft. warm light, typically seen sunrise or sunset. CHB, on the other hand, generate prompts with nonsensical combinations of objects and environments, such 'house
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inside aquarium". While diverse, the prompts are not grounded in reality, which limits their practical use in downst ream tasks.
SC and CCG methods produce more coherent and consistent prompts. However, they show a tendency toward redundancy, particularly in descriptors like 'majestic' and "gallops" for the horse concept, or "charm- ing' and "rustic" for the house concept, reducing the overall uniqueness of the generated prompts. Compared these existing prompt diversification baselines, our proposed prompt generation method, HIRPG, suc- cessfully captures not only the diversity but also the originality and coherence within its generated prompts.
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Effect of number of real samples
DreamCache
DreamBooth
GenOL-ZS (Ours)
Bootpig
HIRPG-FT (Ours)
HIRPG-ZS (Ours)
56
56
ID Aavg
ID Alast
52
52
48
48
10
5
10
# of real examples
# of real examples
OOD Aavg
OOD Alast
32
36
28
32
3
5
10
3
5
10
# of real examples
# of real examples
(a) PACS
DreamCache
DreamBooth HIRPG-FT (Ours)
GenOL-ZS (Ours)
Bootpig
HIRPG-ZS (Ours)
36
28
ID Aavg
ID Alast
32
24
28
20
10
10
# of real examples
# of real examples
12
OOD Aavg
OOD Alast
12
9-
10
10
of real examples
of real examples
(b) DomainNet
Figure 17: Effect of number of real samples on (a) PACS and (b) DomainNet in both ID and OOD accuracy. HIRPG-ZS and GenOL-ZS refer to the zero-shot performance of HIRPG and GenOL, meaning that no real samples are used. HIRPG-FT refers the performance of HIRPG after fine-tuning the SDXL with real examples.
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Concept
Method
Examples
House
LE
A calm house illuminated by the first light of morning.
A sunrise house bathed in warm orange hues.
A dew-drenched house in vibrant sunset colors.
A serene house under a pastel sunrise.
A peaceful house bathed in the soft glow of dawn.
CHB
house, building inside aquarium
house, building inside bakery
house, building inside music studio
house, building inside wave
house, building inside pizzeria.
SC
house => charming, rustic house with ivy-covered walls and thatched roof, nestled amidst flourishing garden of vibrant, blooming flowers, creating a serene and pic- turesque scene.
A charming countryside house with thatched roof and ivy-coverod walls, surrounded by a lush, colorful garden blooming with flowers on a sunny day
A charming rustic house stands besides tranquil pond, surrounded by lush greenery and vibrant blooming plants, reflecting its serene image in the still water.
A charming rustic house, nestled in vibrant, colorful gar- den filled with blooming flowers and tall trees, creating picturesque and cozy scene.
A charming rustic house stands besides tranquil pond, surrounded by lush greenery and vibrant blooming plants, reflecting its serene image in the still water.
CCG
A cozy, red-brick house with white picket fence stands amidst colorful autumn trees.
A cozy, rustic house stands peacefully surrounded by vi-
brant autumn trees and cobblestone path..
A cozy, rustic house nestled in a forest, surrounded by vi- brant autumn leaves and tall trees.
A cozy countryside house stands surrounded by vibrant autumn foliage under clear, blue sky.
A cozy wooden house surrounded by colorful autumn trees under clear blue sky.
HIRPG
A high-contrast black and white photograph of house.
A serene watercolor painting of house.
A vibrant photo of house during sunrise with a warm color palette.
A vivid painting of house using vibrant colors.
A cinematic wide-angle shot of house at dusk.
Table 22: Prompt samples using different prompt generation methods for the concept House from PACS dataset. Trrelevant content, repetitions, and overused phrases are marked in red, brown, and turquoise respectively. HIRPG demonstrates more relevant, diverse, and concise descriptions compared to other prompt generation baselines.
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Concept
Method
Examples
Diving Board
LE
Scuba diver lands the dry seas with his bare feet on
The man with the green board doing darts really nice.
Man in the snow at the beach... the guy on the scuba boat...
Man on the seaboard while diving
A man is caught sea of dolphins scuba diving on a
CHB
diving board, springboard inside home office
diving board, springboard inside bakery diving board, springboard inside music studio
diving board, springboard inside subway station diving board, springboard inside pizzeria
SC
A sleek, modern diving board extends over a pristine pool at an indoor diving facility, brightly lit with natural light streaming through large windows, casting reflections on the water's surface below.
A sleek diving board extends over a shimmering blue swim- ming pool, surrounded by lush greenery and bustling with activity under the bright summer sun.
A sleek diving board extends over oceanview pool, with the shimmering blue waters of the ocean and a clear sky stretching out in the background.
On serene summer day, a sleek diving board extends over sparkling zero-entry pool, inviting swimmers to take the plunge into its crystal-clear, gradually deepening waters. A sleek diving board extends over crystal-clear syn- chronized swimming pool, surrounded by vibrant, chore- ographed swimmers creating mesmerizing patterns in the water.
CCG
A young girl prepares to leap off wooden diving board into a sparkling pool below.
A young girl jumps joyously off colorful diving board into the sparkling blue pool below.
A young girl poised the diving board, ready to leap into the sparkling pool below.
A young girl leaps joyfully off a high diving board into sparkling blue pool.
A young girl stands poised a diving board, ready to leap into the sparkling pool.
HIRPG
A high-contrast black and white photograph of diving board.
A serene watercolor painting of diving board.
A vibrant photo of diving board during sunrise with a warm color palette.
A vivid painting diving board using vibrant colors.
A cinematic wide-angle shot of diving board at dusk.
Table 23: Prompt samples using different prompt generation methods for the concept Diving Board from DomainNet dataset. Trrelevant content, repetitions, and overused phrases are marked red, brown, and turquoise respectively. HIRPG demonstrates more relevant, diverse, and concise descriptions compared to other prompt generation baselines.
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Concept
Method
Examples
The Great Wall of China
LE
The great wall of china completed
The great wall of china
The Great Wall of the China
The great wall of the province of the city of Beijing The Wall China... a building in the countryside
CHB
The Great Wall of China, wall inside home office
The Great Wall of China, wall inside bakery
The Great Wall of China, wall inside music studio
The Great Wall of China, wall inside subway station
The Great Wall of China wall inside pizzeria
SC
The Great Wall of China winds majestically through the dense, vibrant greenery of bamboo forest, creating a striking contrast between ancient. architecture and natu- ral beanty.
The Great Wall of China winds majestically through the landscape, surrounded by ancient historical courtyards that echo with the rich history past dynasties.
The Great Wall of China winds majestically through the dense, vibrant greenery of bamboo forest, creating striking contrast between ancient architecture and natu- ral beauty.
Majestic Great Wall of China winding through lush green hills, while sailboats gently glide across serene lake in the foreground.
The Great Wall of China majestically winds its way through vibrant, sunny meadow, with lush green grass and colorful wildflowers stretching out the foreground.
CCG
The ancient Great Wall China winds through lush green hills under a bright blue sky.
Tourists wander along the ancient, winding Great Wall of China amidst lush mountains.
Tourists hike along the ancient stone path of the Great
Wall winding through green hills.
Visitors hike along the winding, ancient Great Wall of
China amidst lush, rolling green hills.
A majestic stretch of ancient stone wall winds over lush, rolling hills under bright sky.
HIRPG
A high-contrast black and white photograph of The Great Wall of China.
A serene watercolor painting of The Great Wall of China.
A vibrant photo of The Great Wall of China during sunrise with warm color palette. vivid painting of The Great Wall China using vibrant colors.
A cinematic wide-angle shot of The Great Wall of China dusk.
Prompt samples using different prompt generation methods for the concept The Great Wall of China from DomainNet dataset. Trrelevant content, repetitions, and overused phrases are marked in red, brown, and turquoise respectively. HIRPG demonstrates more relevant, diverse, and concise descriptions compared to other prompt generation basclines.
Table 24:
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A.28 Expanding GenOL to the Joint Training Setup
We extend our proposed GenOL to the standard learning setup joint training setup), where all concepts to be learned are provided at once. In this setting, we compare GenOL not only with training-based methods, such as GLIDE, but also with training-free methods (i.e., CLIP-ZS (Radford et al., 2021 SuS-X-
SD [Udandarao et al., 2023), VisDesc (Menon & Vondrick 2023), SD-CIf (Li et al., 2023a], and CUPL Pratt
jtt al. 2023)) that leverage pre-trained Vision-Language Models (VL.ss), such as CLIP (Radford et al. 2021)
or generative models, such as SDXL (Podell et al. (023). Note that although these methods do not update model weights, they generate images for support sets create customized prompls using LLMs classify the target concept. We provide detailed explanation of training-frre baselines in Section A.10
We first compare these methods using the same model, i.e., ResNet-50-CLIP, CLIP model with ResNet-50 as the vision encoder. Specifically, we utilize the YFCC100M (Thomee et al. 2016) pre-trained CLIP model. We summarize the results in Table 25 For with the same amount of data across all basclines for a fair comparison. As shown in the table, GenOL significantly outperforms existing name-only classification baselines, as well as combinations of baselines with our proposed data ensemble method, i.e. CONAN. Furthermore, compared to diverse prompt generation baselines (LE, CHB, SC, and CCG), proposed HIRPG outperforms in both setups seups-with with and without CONAN demonstrating the effectiveness of our proposed components in joint training setup.
methods, we train the model for 10 epochs
Next, we compare the results with those obtained using only the CIIP-pretrained ResNet-50 for training- dependent methods. While the CLIP model can be employed for braining-dependees methods, training VLMs demands substantial computational resources, compared to training- free baselines, hindering real- time adaptation and limiting their deployment in real-world applications(KK0 et al.) 2021 Caccia et al., 2022). Therefore, to improve training efficiency and enable faster adaptation to newly encountered concepts, we also compare the results of training solely on the vision encoder of the CLIP model for training-dependent methods, To assess training efficiency, we train them for 10 epochs, consistent with Table 25 and summarize
the results in Table 26
As shown in the lable, several training-free methods outperform GenOL in the in-domain (ID) scenario. This advantage arises because they utilize off-the-shelf CLIP models, which are pre-trained on large-scale datasets, particularly in the photo domain, which we consider as ID in our experiments. However, despite the benefits of large-scale pre-training, these methods struggle to generalize in OOD scenarios.
In contrast, GenOL. not only outperforms all baselines but also surpasses model trained with manually annotated data in the OOD domains of PACS, CIFAR-IO-W, and DomainNet. This demonstrates that large-scale pre-training alone does not guarantee good generalization across all downstream tasks in various domains, highlighting the necessity of few-epoch training for personalization and real-time adaptation in the namo-only sctup.
A.29 Qualitative Analysis
We qualitatively compare web-scraped images, manually annotated images, and GenOL-generated images, highlighting diversity and recognizability of GenOL-generater images.
we quali- tatively compare samples acquired through different data acquisition methods for the given concept in the Bongard-OpenWorld datasets, as shown in Figure 18 In Figure 18 GenOI effectively generates both positive and negative support sets. In contrast, web-scraped data include images that do not match the given concept 'A leopard relazing on tree branch.' This discrepancy arises from the lengthy and free-form concepts used in Bongard OpenWorld, such as descriptive sentences, compared to the simpler object-action combinations in Hon. In web scraping, those detailed and lengthy search queries may yield unrelated results.
Multi-modal Visual-concept Incremental Learning Setup. In addition to Section 5.2
Note that GenOL relies solely on positive concepts, as mentioned in Section 5.2 Specifically, in manually annotated (MA) data, high-quality annotators not only select positive support sets but also curate hard negative examples for the negative sets. In contrast, GenOL utilizes only positive concepts (i.e., concepts that the model needs to learn) and automatically generates hard negative examples using text prompts created
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CIFAR-10-W
DomainNet
Type
Training Data
OOD
ID
OOD
CLIP-ZS Radford et al.
2021
14.69
5.17
4.9
SuS-X-SD Udandarao et al.
2023
53.08
20.06
7.5
VisDesc
Menon & Vondrick
2023
51.83
16.87
6.52
Training-free freee
CuPL Pratt ct al.
2023
50.5
18.25
6.36
CALIP Guo et al.
2023
51.62
16.43
6.39
SD-CIf et al.
2023a
52.48
12.27
11.85
Glide-syn (He et al.
2023
55.93
38.26
9.31
LE He et al.
2023
73.51
47.43
14.7
(+) CONAN
75.13
52.87
17.26
CHB Saryyldız et al.
2023
70.61
45.28
14.62
(+) CONAN
75.96
52.31
17.49
Training-depa dependent
SC Tian et al. 2024a
71.3
40.42
12.36
(+) CONAN
75.04
49.64
15.19
CCG (Hammoud et al. 2024
58.25
39.32
11.37
(+) CONAN
63.14
42.94
14.37
HIRPG
74.47
52.30
20.18
(+) CONAN (Ours)
77.64
54.85
22.66
Manually Annotated
59.12
71.13
20.29
Table 25: Quantitative comparison between different name-only baselines on joint training setup. We employ the YFCC100M pre-trained ResNet50-CL.P,, which uses ResNet50 as the vision encoder for the CLIP model, for all methods.
by large language models (LLMs), as demonstrated Section A.6 Nonetheless, as shown in Figure 18 the negative samples generated by GenOL are not clearly distinct from the positive examples, which enhances the model's ability to differentiate between the concepts.
Class Incremental Learning Sctup. We compare samples acquired through different generative name- only basclines in the CIL setup, as illustrated in Figure 19 and Figure 20
A.30 Manual Annotation vs. Web-Scraping vs. Generative data
In modern deep learning, the trajectory of advancement is heavily influenced by the exponential growth of training data and the corresponding models trained on these vast datascts. Foundation models are typically exposed to datasels in the order of billions during training, obtained predominantly through web scraping
(Schuhmann et al. 2022 Xue et al. 2020 Zhu et al. 2024 Gao et al. 2020 Kocetkov et al. 2022 Bain et al. 2021). Although web scraping is cost-effoctive method to produce high-quality datascts, studies underscorc Packer al. 2018 Caliskan el. al. 2017). copyright, privacy, and license concerns (Quang 2021 Solon 2019 J, and the risks of data contamination (Dekoninck et al. 2024
issues such as potential biases (Foerderer 2023
Li 2023) or data leakage from evaluation (Blllccuu et al. 2024).
As demonstrated in Figure I we highlight the key differences between Manually Annotated (MA), Web scraped, and Generated data on six different. axes: (a) Controllability, (b) Storage issues, (c) Usage restric- tions, (d) Privacy issues, (e) Acquisition cost, and (f) Noise. In this section, we aim to provide the definition of each of these axes and their corresponding implications on each type of data source.
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PACS
DomainNet
CIFAR-I0-W
Type
Training Data
ID
OOD
ID
OOD
OOD
CLIP-ZS Radford et al.
2021
99.11
49.12
14.69
5.17
57.14
SuS-X-SD Udandarao et a.
2023
95.55
47.81
20.06
7.5
53.08
VisDesc Menon Vondrick
2023
93.77
46.09
16.87
6.52
51.83
Training-free
CuPL Pratt al.
2023
89.32
46.51
18.25
6.36
50.50
CALIP Guo et al.
2023
92.58
48.43
16.43
6.39
51.62
SDDCIf La al.
2023a
92.58
48.43
12.27
11.85
52.48
Glide-syn (ee et al.
2023
85.16
33.2
29.02
6.73
55.93
(He al.
2023
88.43
38.03
40.74
10.47
73.51
(+) CONAN
93.47
44.54
54.60
15.62
75.13
CHTB Sanyildiz et al. 2023
83.38
30.98
35.97
9.60
70.61
(+) CONAN
92.88
41.42
49.17
15.51
75.96
Sriningg-ependeer
SC Tian et al.
2024a
76.26
28.19
30.42
8.23
71.30
(+) CONAN
85.46
42.05
44.66
12.01
75.04
CCG Hammoud al.
2024
81.01
31.71
26.59
6.89
58.25
(+) CONAN
85.76
41.55
32.31
8.72
63.14
HIRPG
89.91
43.98
46.19
17.80
74.47
(+) CONAN (Ours)
94.36
60.75
51.85
21.01
77.64
Manually Annotated
97.03
33.80
72.54
19.09
59.12
Table 26: Quantitative comparison between different name-only baselines on joint training sctup. We employ the YFCC100M pre-trained ResNet50- for training-free mothods, while for training- dependent methods, utilize only the vision encoder of the CLIP model.
Positive
Negative
MA
Web
GenOL(Ours)
Figure 18: Samples using different data acquisition methods for the same concept in the MVCIL setup. The given concept is 'A leopard relazing on free branch' from the Bongard-OpenWorll benehmark. The left four images represent positive examples that depict the given concept, while the right four images represent negative examples that illustrate different concepts. Here, 'MA' refers to manually annotated data.
Controllability. encompasses the ability to generate or acquire images with various contexts, backgrounds, settings, and themes as desired. It pertains the ability obtain images depicting different concepts compositions not commonly found in natural environments, as well as in domains relevant the task hand. Under this definition, we assert that the MA data exhibit low controllability This limitation arises from its reliance on data captured from a finite set of scenarios or sensors, which inherently restricts the breadth of diverse settings where the concept can be observed. Web-scrapped data also suffer from low controllability for the same reasons. In contrast, the generated data have high controllability due to the ability of foundation text-to-image (T2I) generators to produce diverse images for each concept through varied prompting.
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Dog
Person
House
HIRPGFCONAA (Ours)
HIRPG
LE
50
CCG
CHB
Figure 19: Samples using different data acquisition methods for the same concept in the CIL setup. The given concepts are Dog, Person, House, which are from PACS. Our proposed GenOT, i.e., HIRPG + CONAN, produces a more diverse of images that comprehensively cover various domains, ensuring broader representation and adaptability. In contrast, other methods tend to generate similar (e.g, Dog in CCG) Nomain-limite (e.g, House in SC) images.
Storage Issues. Storing extensive data, locally or in the cloud, imposes additional costs, which can become impractical in enviromments constrained by limited total storage capacity. In addition, transmitting large, substantial data samples in a federated setup can face challenges arising from bandwidth and latency bot- tlenecks. In such contexts, depending on large corpus of MA data becomes counterintuitive. On the other hand, both web-scraped and generated data present themselves as cost-effective alternatives for accessing substantial data volumes without necessitating explicit. storage expenditures.
Usage Restrictions. encompass limitations imposed on the use of images for training machine/des deep learning models, typically due to copyright licensing protections. These restrictions arise from various
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Ambulance
Monkey
The Great Wall
HIRPG (Ours)
HIRPO
LE
SC
CCG
CHB
Figure 20: Samples using different data acquisition methods for the same concept in the CIL setup. The given concepts are Ambulance, Monkey, The Great Wall, which are from DomainNet. Our proposed GenOL, f.e., HIRPG CONAN, produces more diverse set of images that comprchensively cover various domains, ensuring broader representation and adaptability. In contrast, other methods tend to generate similar (e.g., The Great Wall in LE) limine-limited (e.g., Ambulance in CCG) images.
legal frameworks across different. demographics, regulating, and sometimes prohibiting, the training of models protected data for commercial deployment. This challenge is particularly prevalent in web-scraped data, where the abundance of protected data may not be adequately filtered Khan & Hanna 2020). In contrast, MA data bypass this issue, as it is presumed that the data are filtered or obtained from a proprietary source with appropriate permissions during annotation. Notably, generated data offer a more advantageous position, as they do not necessitate such filtering and encounter limited or no usage restrictions, thereby providing a readily available solution issues arising from data protection concerns.
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Privacy Issues. may arise when data samples inadvertently leak explicitly contain sensitive, con- fidential, or private user information. Examples of such images could include those leaturing people's faces O'Suullivn 2020 Murgia & Harlow 2019) or personal objects that disclose identity-related details, such as addresscs or financial assets. Once again, web data emerge as the primary source vulnerable to issues stemming from the use of private data, an issue extensively present in web-scraped data (Subramani et al. 2023: Wenger et al.) 2022 Solon 2019 Lukas et al., 2023). MA data are generally protected from privacy concerns due to prior filtering or explicit agreements on data usage before annotation. While generative
models may also pose privacy risks, recent studies (Chen et al.
2018 2019 Carvalho et al. 2022 Xu et al,, 2023) have explored methods to enforce fairness and differential privacy in data synthesis, providing solutions
for identity protection.
Acquisition Cost. refers to the total expenses incurred in obtaining a specific mumber of data samples necessary to train or evaluate the learner fe for a particular task. As emphasized in Section [| MA data entail a substantial acquisiiinn cost, primarily due to the expenses associated with densely annotating the data through human workers. This, coupled with the rigorous filtering process, makes MA data prohibitively ex- pensive to acquire at scale. Although web data do not require such significant financial outlay for annotation, they do require intensive filtering, which contributes to an elevated cost and poses barrier to constructing large datasets solely from web sources. In contrast, due to the advantages in controllability, generated data boast a notably low acquisition cost for generating large and diverse datasets.
Noise. pertains to instances where data that are not related to a concept are erroneously labeled as belonging that concept. It may also mean discrepancies between the context of the data and the associated concept. As highlighted in Section [A18] web data often exhibit a high degree of noise, necessitating extensive filtering label correction processcs. In contrast, both MA data and generated data are less susceptible to such noise. In the case of MA data, the presumplion of prior filtering serves a primary solution to mitigate noisy data, Meanwhile, for generated data, the advantages of controllability enable the mitigation of noise resulting from inconsistencies in concept-image alignment. Despite the drawback of requiring GPU usage, T2I model inference incurs lower costs compared to MA due to its ability to generate pure images, making it a more cost-effective option.
A.31 Extended Quantitative Analysis
We perform additional comparisons with various combinations of diverse prompt generation baselines and data ensemble methods on DomainNet, and summarize the results in Table 27 For all data ensemble methods, including our proposed CONAN, fair comparison. As shown in the table, CONAN is not only effective when combined with HIRPG, but also shows strong performance when paired with other prompt generation baselines, demonstrating its plug-and-play applicability across various mothods.
select an equal number of samples for the coreset to ensure
A.32
Details of RMD Score Calculation
The RMD Cui et al.
2023 score of a sample (I,, y1) is defined as follows:
R.MD(1 W:) Mas(xri vi) - Magn
Mes (2i, vi) =- (G((x) uy:) 1' -ー (G(xz) p
(11)
Magu (xt) = (G(x,) - Magn) )T Zagn (G(z;) - Magn):
where G represents the feature extractor, Mole 84) denotes the Mahalanobis distance from Giz;;) to the corresponding class mean vector My. N. Eg G(æ,), with N; being the count of samples labeled as yer
x-1 denotes the inverse of the averaged covariance matrix across classes. Furthermore, Magn(z,) represents the class-agnostic Mahalanobis distance, where Magn denotes the overall sample mean, and agg denotes the inverse covariance for the class-agnostic case.
In the ouline CL setup, where data arrive a continuous stream, it is not feasible to calculate j and Σ of the entire dataset. Instead, a necessity arises to continuously update these statistical parameters accommodate the dynamic nature of the incoming data stream.
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ID
OOD
Method
AAUC 1
Aust 1
AAUC
Atst
LE
20.01
15.38+0.31
6.40-1..13
4.59+:0.09
(+) Uncertainty
14.66+0.30
9.40++0114
4.85++0.08
3.08+0.06
(+) CRAIG
28.64+0..55
23.91+00.27
8.53++0.27
6.83+0.07
(+) Glister
17.53=0.44
11.57=0..25
5.67
0.61
(+) GradMatch
27.68+0.68
22.89:1.144
8.57:10.31
6.86+00.07
(+) AdaCore
24.73+0.56
18.95++0.25
7.62++0.17
5.53+00112
(+) LCMat
27.7220.49
23.10:::223
8.50::.22
6.84+0.02
(+) Moderate
21.33+0.54
15.91
6.47
0.56
(+) CONAN
30.80:10.63
25.33+0.200
9.54+10.25
7.59+0.17
CHB
16.6910.16
13.45=00199
5.61
4.18
(+) Uncertainty
11.15+0.35
7.06+0.15
3.97
2.41
(+) CRAIG
26.42:10.35
22.4910.33
8.1190.09
6.61
(+) Glister
14.07=0.13
9.38+0.06
4.68:00.55
2.98
(+) GradMatch
25.20=0.36
21.58::0.27
7.97
6.51
(+) AdaCore
22.2910.31
17.2710.16
7.2340.09
5.27
(+) LCMat
24.99=10377
21.46:10.24
7.99:0.12
6.68+0.10
(+) Moderate
18.64:0.24
13.96:00100
5.92:00.07
4.08
(+) CONAN
29.06=0.37
24.5210.17
9.28110.14
7.56:0.11
SC
11.89+00.7
8.66+0.20
3.90+0.00
2.68+0.04
(+) Uncertainty
10.32+00.26
6.41+0.20
3.17
1.87
(+) CRAIG
20.05=:0.25
17.13+0.11
6.02+0.12
4.88
(+) Glister
11.30+00.24
7.24+0.08
3.42+00.07
2.10
(+) GradMatch
19.83+0.38
16.82+00119
5.94+0.11
4.78+0.08
(+) AdaCore
17.67
13.29
5.1910.13
3.69
LCMat
19.86=10.32
16.79:1.229
5.98+0.11
4.77
Moderate
14.17:00.24
10.34
4.03+00.0
2.72+0.05
(+) CONAN
22.3610.34
19.1310.32
6.7110.15
5.4810.13
OCG
12.55+0.22
10.2110.26
4.03+00.10
2.91+0.110
(+) Uncertainty
14.73+0.41
10.65
4.48
3.13
(+) CRAIG
16.72=0.27
14.23+10.30
5.16::0.08
4.11
(+) Glister
14.31
10.54
4.4610.12
3.08
(+) GradMatch
16.75=10.26
14.3110.21
5.1511.111
4.10
(+) AdaCore
17.11:0..36
13.8740.18
5.20+1011
3.9340.11
(+) LCMat
16.71#0.23
14.08+10.26
5.15+00.09
4.05
(+) Moderate
14.52:10.29
11.0110.14
4.40+0.10
3.15
(+) CONAN
18.32++0.22
15.83-0.33
5.78+10.17
4.70+0.11
HIRPG (Ours)
27.72++0.00
23.71+0.39
10.70+0.119
8.75+0.113
(+) Uncertainty
21.90+0.37
15.70+0.00
10.01+0.23
7.1910.11
(+) CRAIG
32.53+0.20
28.4440.22
13.25+0.155
11.53+0.06
(+) Glister
23.16+0.37
16.98+00.35
10.56+0.26
7.60
(+) GradMatch
32.53+0.43
28.36:1041
13.4840.31
11.74+0.18
(+) AdaCore
32.15+0.55
26.83+0.18
13.62+0.27
11.37+0004
(+) LCMat
32.38+40.44
28.36+40.22
13.42+0.26
11.76++0.17
Moderate
25.5740.42
20.3840.16
10.5340.29
8.1740.13
(+) CONAN (Ours)
34.60+0.31
30.09:0.11
14.5310.22
12.65-10.09
Table 27: Quantitative comparison between data selection methods with different diverse
prompt generation baselines on DomainNet. Uncertainty, CRAIG, Glister, GradMatch, Adacore, and LCMat require fine-tuning on the full dataset to compute gradients of the finc-tuned model, despite us- ing a pre-trained model for initialization. contrast, Moderate and CONAN do not require any fine-tuning.
59

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Published in Transactions on Machine Learning Research (10/2025)
k-lowest RMD ensemble
CONAN (Ours)
01,0 22 E D
Ø1,
32 E D
Class
Class centroid
Decision boundary
Figure 21: CONAN helps in finding a tighter decision boundary due to having a higher probability of including high RMD scored samples in the ensemble of generated data D. Intuitively, high RMD scored samples are farther away from their class prototype but closer other class samples comparison low RMD scored samples which concentrated closer around the class prototype. Note that CONAN includes not only high RMD samples but also some low RMD (i... class-representatiee) samples.
Starting with the initially computed mean vector My; and the covariance matrix E from N samples, the arrival of a new sample æN-1 triggers an update. The updated mean vector Unew is computed incrementally using a simple moving average (SMA), as follows:
N Noold + IN+1
fnew
(12)
+1
Similarly, we calculate Σ using a simple moving variance. Specifically, the update for the new covariance matrix Enew is calculated using the deviation of the new sample from the old mean A ÆN+1 Holds and its deviation from the new mean Anew = IN+1 Mnew.
Formally, we formulate the update process as follows:
Enew N N
(NEold + AAT new)
(13)
The update process for the class-agnostic mean vector Magn and covariance Engn follows the same incremental approach as described for the specific components.
CONAN includes many samples with high RMD scores in the ensemble dataset. Not only does it include samples with the highest RMD scores, but it also probabilistically incorporates samples with low RMD scores. This approach ensures core-set ensemble, and we illustrate the effect of CONAN in Figure 21
A.33 Scaling Behavior
Recent scaling law studies (Hernandez al. 2021 Hoffmann al. 2022 offer predictive insight into model performance by scaling computation, data, and model capacity. Despite the limited exploration of scaling continual learning settings (Ramasesh et al. 2022) and particularly with synthetic data (Fan al.) 2024) being confined to static frameworks, our empirical analysis in Figure 22 delves into scaling dynamics with varying proportions of generated data for online continual learning setup.
For ResNet18 (He et al. 2016) and ViT (Dosovitskly & Brox 2016), we observe a consistent linear improve- ment trend in both ID and OOD AAUC as the volume of generated data increases, across the PACS (Zhou et al. 2020) datasct. This scaling behavior underscores the positive correlation between performance provement and larger generated data ensembles online continual learning, reinforcing the rationale for the use of generators in the absence of annotated data.
60

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Published in Transactions on Machine Learning Research (10/2025)
ResNet-18
VIT
45
100.7 0.98
35
= 100.26 26 1.23
40
OOD AAUC
35
30
30
25
25
20
20
30
40
50
60
35
40
45
50
55
ID AAUC
ID AAUC
x 0.125
x 0.25
x 0.5
x 1
x
x
Ensemble scaling behavior of (a) ResNet18 (He et al., 2016) and (b) VIT (Dsoovitskiy & Brox, 2016)) for ID AAUC OOD AAUC the PACS dataset (Zhou et al. 2020) using ER (Rolnick et al., 2019). A consistent linear improvement trend exists in both ID and OOD AAUC the volume of generated data increases. (x 1) denotes the ensemble volume in primary experiments, the default data budget.
Figure 22:
A.34 Analysis of Similarity between Prompts Generated by HIRPG
We empirically demonstrate that the overlap between generated prompts from different nodes is rare by measuring the similarity of prompts generated at each node. To measure similarity, we first construct a K-ary Tree with K = and D = Next, we divide the generated prompts into 7 groups, where i-th group refers to the set of prompts generated by RPG using the prompt of i-th node at depth 1 is used as the initial negative prompt (í.e., Pa). We then measure the group-wise similarity by calculating the average of all pairwise similarities between prompts generated from each node, where the similarity between prompts is measured by the cosine similarity of their text embeddings extracted by Sentence-BERT (Reimers 2019). We summarize the grsup-wise similarily of generated prompts in similarity matrix, as shown in Figure
As shown in the similarity matrix, the similarities between the same nodes (i.e., antidiagonal elements) are generally low. This is because prompts generated at the same node are iteratively generated while considering each other as negative examples. Moreover, similarities between different nodes (i.e. non- antidiagonal elements) are also generally low, although slightly higher than the antidiagonal elements. We characteristic of LLM, where different examples provided during in-context
believe this is attributed learning lead to varied outputs (Su et al. 2022 Agarwal et al. 2024). Specifically, since RPG each node begins with distinct initial negative examples passed from the previous depth, it generates different prompts, even though the model cannot directly reference prompts generated by other nodes as negative examples.
A.35 Broader Impact Statement
Our work involves generating training data using generative models, which may raise potential concerns regarding privacy and bias. To mitigate privacy concerns, we exclude any person-related generated data from the released dataset, as discussed in Section 7 We believe that advancing privacy-preservee (Xu all,
2023
Chen & Yan 2024) and differential privacy (Chen et al. 2023a) generative models would effectively
mitigate these concerns.
Regarding bias, the generated data contain bias and the model continually trained on such data, the bias is likely to be amplified over time. Although we demonstrate Fig and Fig of Section (53 that GenOL is diverse image generation implicitly leads to producing less-biased data than MA and other generative baselines, explicit bias-prevention strategies are essential to mitigate the risk of generating harmful biases.
61

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Published in Transactions on Machine Learning Research (10/2025)
Similarity between Prompt Groups
0.60
6
0.44
0.44
0.46
0.43
0.43
0.44
0.40
5
0.46
0.47
0.47
0.42
0.42
0.42
0.44
0.55
Node Index
4
0.43
0.46
0.45
0.45
0.41
0.42
0.43
0.50
3
0.47
0.47
0.47
0.39
0.45
0.42
0.43
0..45
2
0.45
0.47
0.43
0.47
0.45
0.47
0.46
1
0.47
0.43
0.47
0.47
0.46
0.47
0.44
0.40
0
0.41
0.47
0.45
0.47
0.43
0.46
0.44
0.35
0
1
2
3
4
5
6
Node Index
Figure 23: Similarity matrix of prompts generated at each node in HIRPG in K-ary structure with D = and K = on DomainNet. Node index i refers to the set of prompts generated by RPG, where the prompt of the i-th node at depth 1 is used as the initial negative example. We measure the similarity between two nodes by calculating the average of all pairwise similarities between prompts generated from each node. The similarity between prompts is measured by the cosine similarity their respective text embeddings.
which can be critical for real-world deployment. We believe that editing bias-inducing layers or paramoters in generative models (Wang et al. 2024 Xu et al. 2025)), as well as fine-tuning generative models trained with small and unbiased datasets (Malakouti & Kovashka, 2025), can effectively prevent bias propagation in GenOL and reduce the risk of deploying biased models.
A.36 Failure Case Analysis of GenOL
While GenOL produces higher-quality and less noisy data than other generative and web-scraping baselines (see Sec[A.29] sometimes generates unnatural images. For example, in the 'People embracing each other' row of Bongard-OpenWorlld (Fig. 24], GenOT. produces implausible arm and leg arrangements. Similarly, in the 'Keyboard Piano' row, the generated keyboard contains distorted keys that deviate from a standard piano keyboard. In Bongard-HOI, similar issues appear: in 'Eat Banana' and 'Kick Sports Ball', human limb positions unrealistic, and in the third column of 'Kick Sports Ball', figure with three legs is generated. Likewise, in 'Hug Dog', instead of the intended concept of person hugging a dog, GenOL sometimes generates a dog hugging another dog. These cases highlight that text-to-image generative models often require highly detailed textual descriptions to faithfully capture the intended concept.
Despite these imperfcctions, we argue that the inclusion of such unnatural images in the training set can be beneficial. Specifically, we believe that exposure both natural and imperfect samples promotes robustness and improves generalizability, which contributes the strong out-of-domain performance reported in Table
62

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Published in Transactions on Machine Learning Research (10/2025)
Bongard-HOl
Bongard-Openworld
Keyboard
Banana
Piano
Eat
People embracing east other
Hug Dog
Kick Sports Ball
Variety Aaptons
Figure 24: Failure case of GenOL. GenOL occasionally produces implausible arm leg configurations and distorted visual outputs.
Table and Table Consequently, models trained with data generated by GenOL are better equipped to handle noisy or imperfect images, facilitating more reliable real-world deployment.
63


Reviews:
[{"review_id": "QAJiYnwK9u", "reviewer": "Reviewer_wpN7", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n1. Practically important problem: Manual annotation costs are a genuine bottleneck in continual learning. This paper addresses this limitation with the help of pretrained generative models.\n\n2. Comprehensive evaluation: Extensive experiments across multiple datasets with thorough ablation studies.\n\n3. Strong empirical results: Consistent improvements over baselines, particularly on out-of-distribution domains.\n\n**Weaknesses**\n\n1. Missing critical baselines: No comparison with few-shot learning using, say, 1-5 real examples, which is the most obvious practical alternative, considering the fact that the proposed approach is heavily dependent on the pretrained models.\n\n2. Limited technical novelty: Combines existing techniques (hierarchical generation, difficulty-based selection) without fundamental algorithmic advances.\n\n3. Experimental design concerns: ID/OOD splits may favor synthetic data; evaluation focuses on standard visual concepts.\n\n4. Foundation model dependency: Performance is bounded by pre-trained model capabilities with no learning mechanism and/or continual adaptation, which is a crucial aspect of any CL approach.\n\n5. Scalability issues: Requires substantial computational resources; limited analysis of deployment costs.\n\n**Additional Comments**\n\n1. HIRPG is well-motivated but fundamentally limited by underlying LLM capabilities.\n\n2. CONAN's RMD-based selection is reasonable but lacks comparison with sophisticated active learning approaches\n\n3. Missing failure case analysis and human evaluation of generated image quality", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper proposes GenCL for continual learning using only concept names, combining hierarchical prompt generation (HIRPG) with complexity-guided data selection (CONAN). The work addresses a relevant problem with solid experimental methodology but has notable limitations.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "**Add Critical Missing Baselines**\n\n1. Few-shot learning: Compare with 1, 3, 5, 10 real examples per concept (most obvious practical alternative).\n\n2. Modern continual learning methods: Include recent prompt-based and parameter-efficient approaches.\n\n3. Stronger web-scraping: Use more sophisticated data collection beyond the basic 3-search-engine approach.", "broader_impact_concerns": "**Inadequate Broader Impact Statement**\n\n1. The authors have included a broader impact section, however, it does not address the concern of potentially present biases in generative and/or LLMs. Furthermore, generative models can potentially generate harmful and/or biased content, which requires careful removal from the dataset. The authors do not clarify how to mitigate such an issue.\n\n2. In continual learning scenarios, biased synthetic data could lead to progressive bias accumulation as new concepts are learned, potentially amplifying discrimination over time. The automated nature of the generation process means biases could propagate unchecked across multiple learning episodes. Additionally, the approach could enable rapid deployment of biased models in real-world applications without adequate human oversight, particularly problematic in sensitive domains like hiring, lending, or law enforcement, where continual learning systems might be deployed.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "EUPPJmDwcw", "reviewer": "Reviewer_4rEq", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strengths**\n\n1. Addresses a Practical Bottleneck: Tackles the high annotation and data curation cost in online continual learning by operating in a \"name only\" regime, highly relevant for real-world deployment scenarios.\n\n2. Solid Empirical Validation: The experiments demonstrate that GenCL not only outperforms web-scraped baselines but also surpasses fully supervised models, especially on OOD test splits, showing strong generalization.\n\n3. Innovative Diversification Techniques: The proposed HIRPG and CONAN address key challenges in data diversity for synthetic training, as seen through both quantitative and qualitative analyses.\n\n4. Bias and Fairness Analysis: The paper goes beyond accuracy focus, quantifying gender, race, and geographic bias and demonstrating GenCL generates more demographically balanced data than other automated or manual methods.\n\n**Weaknesses**\n\n1. Overreliance on Existing Popular Generative Models: The success of GenCL is fundamentally tied to the coverage and bias profile of the pre-trained text-to-image generators. While Section 7 briefly addresses this, the main results focus on concepts these generators already \"know,\" not genuinely out-of-vocabulary or low-resource concepts. The “future work” section mentions this gap, but stronger discussion or a limited experiment on rare/unseen concepts would add value.\n\n2. Limited Innovation Relative to Prior Generative Replay: The use of generative models for continual learning, as well as replay and coreset selection, is well-trodden territory. The main novelty lies in integrating recent T2I models with hierarchical LLM-driven prompt diversification and diversity-based ensembling, but the core conceptual structure is relatively incremental.\n\n3. Prompt Engineering Complexity Not Fully Quantified: Although HIRPG is presented as efficient and diverse, the effect of prompt parameters, temperature, and tree depth on diversity and downstream accuracy is not thoroughly dissected in the main paper. Computation/memory costs are said to be reported in the appendix, but main-text quantitative results on cost vs. benefit are lacking.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces GenCL, a framework for \"name-only\" continual learning (CL) that leverages generative models to synthesize training data given only the names of new concepts, removing the need for manual annotation or web-scraping. The central innovations are (1) HIerarchical Recurrent Prompt Generation (HIRPG), a prompt diversification technique that uses in-context LLM prompting to generate diverse text inputs for text-to-image models, and (2) COmplexity-NAvigating eNsembler (CONAN), an ensemble method that selects a diverse, minimally redundant coreset from multiple generative models using a relative Mahalanobis distance scheme. Empirical results show GenCL outperforms both web-scraped and even annotated data on various CL benchmarks, achieving higher out-of-distribution (OOD) performance and demonstrating lower demographic biases in generated data.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please see strengths and weaknesses.", "broader_impact_concerns": "This work involves generating training data using generative models, which may raise potential privacy concerns. To mitigate such risks, this work excludes any person-related generated data from the released dataset, as discussed in Section 7. I believe that advancing privacy-preserving and differential privacy generative models would effectively mitigate these concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "IJ65e4QslG", "reviewer": "Reviewer_XSeX", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\n- The idea to bring generative models to the name-only continual learning setup is novel and has multiple advantages compared to web search. The mechanisms to ensure diversity: hierarchical prompt generator and coreset selection are also new, very effective in the proposed evaluation setup, and could give inspiration to other generative work.\n\n- Many implementation details for full reproduction of the experiments are available in the appendix. The method is compared to many baselines on a wide set of benchmarks, ensuring the validity of the approach.\n\n\nWeaknesses:\n\n- Small presentation detail: in the table I would prefer that method names are followed by citation links rather than conference where the work was published.\n\n- Why leaving Imanet-R results in Appendix ? This is a larger scale dataset compared to the others, and the results are good so it would make sense to put these results in the main text. Maybe because of the lack of baselines ? Why not run more baselines on Imagenet-R ?\n\n- My understanding is that most of the results presented in the paper are run by the author themselves. Can you clarify in the text whether this is the case, and for which results ? Why not use numbers in the concurrent papers ?\n\n- The name-only continual learning setup is very niche and feels very artificial, is it related to any real-world application ?\n\n- The use of generative models is not necessarily better than scrapping the web in terms of diversity / privacy / licences. This highly depends on the choice of generative model, the data it was trained on ect. The paper mentions: “However web-scraped data suffer from noise, low controllability, and usage restrictions”, the exact same can apply here. The performance of the model is also highly dependent on the choice of generative model. For example, the paper uses GPT-4o, but how would the performance change if another model was used instead ? Finally, in this case GPT-4o is proprietary, that means less control over what data is used and generated and less understanding of how the model works. I would like to see a discussion or additional experiments with different generative models.\n\n- The introduction should put a bit more emphasis more on the fact that this paper is targeting continual learning with new concepts, as opposed to continual learning under unknown distribution of known concepts.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents a new method for the problem of name-only continual learning. Instead of manually annotating samples for new concepts, or fetching samples from the web, new samples are generated using a complex prompt generative module coupled with a new ensembling technique, both ensuring diversity in the generations. The proposed method sets a new SOTA in name-only continual learning on various benchmarks such as PACS or DomainNet.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Major points are:\n- Clarifying how the results were run (taken from concurrent papers as opposed to rerun by authors), and explain why\n- Study and discussion on the impact of different generative models\n\nOther clarification and presentation points in weaknesses are minor.", "broader_impact_concerns": "No ethical concerns specific to this work, except the use of generative model its associated well-known issues.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)