Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
Authors: Lan Li
Da-Wei Zhou
Han-Jia Ye
De-Chuan Zhan
Venue: ICML 2025 poster (2025)
Keywords: Domain-incremental learning，Class-imbalanced learning
Primary area: deep_learning->algorithms
TL;DR: We propose the Dual-Balance Collaborative Experts (DCE) framework to address intra-domain class imbalance and cross-domain class distribution shifts in domain-incremental learning.

Abstract: Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge. DIL faces two critical challenges in the context of imbalanced data: intra-domain class imbalance and cross-domain class distribution shifts.  These challenges significantly hinder model performance, as intra-domain imbalance leads to underfitting of few-shot classes, while cross-domain shifts require maintaining well-learned many-shot classes and transferring knowledge to improve few-shot class performance in old domains. To overcome these challenges, we introduce the Dual-Balance Collaborative Experts (DCE) framework. DCE employs a frequency-aware expert group, where each expert is guided by specialized loss functions to learn features for specific frequency groups, effectively addressing intra-domain class imbalance. Subsequently, a dynamic expert selector is learned by synthesizing pseudo-features through balanced Gaussian sampling from historical class statistics. This mechanism navigates the trade-off between preserving many-shot knowledge of previous domains and leveraging new data to improve few-shot class performance in earlier tasks. Extensive experimental results on four benchmark datasets demonstrate DCE’s state-of-the-art performance.

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
Lan Li Da-Wei Zhou 12 Han-Jia Ye"
De-Chuan Zhan 12
Abstract
domain imbbllance
Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge. DIL faces two critical challenges in the context of imbal- anced data: intra-domain class imbalance and cross-domain class distribution shifts. These chal- lenges significantly hinder model performance, as intra-domain imbalance leads to underfitting of few-shot classes, while cross-domain shifts re- quire maintaining well-learned many-shot classes and transferring knowledge to improve few-shot class performance in old domains. To overcome these challenges, introduce the Dual-Balance Collaborative Experts (DCE) framework. DCE employs frequency awarre expert group, where each expert is guided by specialized loss functions to learn features for specific frequency groups, ef- fectively addressing intra-domain class imbalance. Subsequently, a dynamic expert selector learned by synthesizing pseudo-features through balanced Gaussian sampling from historical class statistics. This mechanism navigates the trade-off between preserving many-shot knowledge of previous do- mains and leveraging new data to improve few- shot class performance in earlier tasks. Extensive experimental results on four benchmark datasets demonstrate DCE's state-of-the-art performance.
R.R.R.
distribution shills: Po-1 Po
Concept drife (xly) (a
Figure I. A schematic illustration of imbalanced DIL: In addition to concept drift (e.g.. image style discrepancies). imbalanced DIL exhibits intra-domain class imbalance (varying sample quantity ratios within individual tasks) and cross-domain class distribution shifts (class distribution differences across domains).
et al., 2016; Deng et al., 2009; Floridi & Chiriatti, 2020). However, the real world is inherently dynamic, with data often arriving in non-stationary streams (Aggarwal, 2018; Ye et al., 2024; Zhuang et al., 2023), requiring models to continuously adapt to changes in data distributions (Gama et al., 2014). A core challenge in this context is catas- trophic forgetting, where models forget previously learned knowledge while adapting to new data. To address this, Donain-Incremental Learning (DIL.) has emerged cal paradigm in continual learning, focusing on model adap- tation in the presence of concept drift (Han al., 2021). Recent studies have made significant progress in mitigating forgetting caused by domain shifts by leveraging pre- preitrained models (PTMs) (Wang et al., 2022c;b;a). These approaches typically initialize DIL models using PTMs, freeze their parameters to preserve existing knowledge, and introduce lightweight modules (Jia et al., 2022: Chen al., 2022; Lian et al., 2022) adapt new domains.
criti-
However, class imbalance, pervasive issue in real-world data, remains an underexplored challenge in the presence of concept drift (Yang et al., 2022). For example, in au- tonomous driving systems, traffic sign samples under ex- treme weather conditions are much rarer than those nor- mal scenarios. Class imbalance DIL manifests in two dimensions: intra-domain imbalance and cross domain class distribution shifts, shown in Figure 1.
1. Introduction
In recent years, deep learning has demonstrated powerful representation learning capabilities in various fields, includ- ing computer vision and natural language processing (He
'School of Artificial Intelligence, Nanjing University, China "National Key Laboratory for Novel Software Technology, Nan- jing University, China. Correspondence to: Da-Wei Zhou <zhoudw lamda.nj..cdu.cn>.
Imbalanced CIL introduces two major challenges for ex- isting PTsed DIL methods. First, intra-domain class imbalance causes models to overfit many-shot classes during training, leading to underrepresentation and poor general-
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Addressing Imbalanced Domain- Incremental Learning through Dual-Balanc Collaborative Experts
Muny tbot clases
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Few-sbot classes
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Domain
Domniin
Domain
(a) L2P
S-iprompt
(e) DCE
Figure 2. On the DomainNet dataset, we illustrate the accuracy changes for test samples from the first domain during the training process (demoted by domain bn to bς). The shared prompt method (L2P) benefits from a shared feature space during updates, resulting in improved performance for few-shot classes, but suffers from catastrophic forgetting of many-shot classes. In contrast, the domain-specitn prompt method (S-iprompt) avoids knowledge sharing between tasks, resulting in lower catastrophic forgetting for many-shot classes but no performance improvement for few-shot classes in the new domain. Our DCE reduces forgetting in many-shot classes and improves performance for few-shot classes.
ization on few-shot classes. Second, cross-domain class distribution shifts pose a more complex problem that goes beyond simply mitigating catastrophic forgetting. this set- ting, the model must not only retain well-Icarned knowledge from previous tasks but also effectively transfer new-task information to improve few-shot class performance. How- ever, mechanisms designed to prevent forgetting often inad- vertently suppress the generalization potential of few-shot classes by limiting their ability to benefit from new new-domain data, as shown in Figure 2. This requires DIL methods to simultaneously preserve the performance of well-learned many-shot classes and enhance the generalization of few- shot classes through knowledge sharing across domains.
aware experts to resolve intra-domain class imbalance, com- bined with a dynamic expert selector that balances cross- domain knowledge fusion while mitigating catastrophic for- getting. (3) We establish four benchmarks for imbalanced DIL scenarios, where experimental results demonstrate that DCE achieves state-of ofate-of-the-art performance. Code is re- leased at https:///e /gg thub. com La n81 O/DCF.
2. Related Work
Continual Learning/Incrementall Learning represents a key area in machine learning, with a primary focus on en- abling models to assimilate new knowledge from sequen- tial data streams (De Lange et al., 2021; Masana et al., 2023; Wang et al., 2024: Zhao et al., 2021; Zheng et al., 2024; Qi et al., 2025; Ruru et al., 2024). This field is com- monly divided into three branches: task fask-incremental learn- ing (TIL) (De Lange et al., 2021), class-ineremental learning (CIL) (Masana et al., 2023; Zhuang et al., 2022: 2024), and domaain-nncemmnnall learning (DII.) (van de Ven al., 2022). Both TIL and CIL address challenges associated with the introduction of novel classes, requiring systems to learn new concepts without crasing previously acquired knowledge. In contrast, DIL handles scenarios where the label set remains fixed, but the incoming data originates from new or varying domains (Wang et al., 2022a; Shi & Wang, 2023).
To address these challenges, we propose Collaborative Expert (DCE) with a dual-phase training paradigm. In the first stage, we train multiple frequency- aware expert networks, each specialized in learning repre- sentations for a specific frequency group, which helps bal- ance the representations of many-shot and few-shot classes and mitigates underfitting of few-shot classes. In the sec- ond stage, we develop dynamic expert selector trained synthetic secudd-fcatures. which are generated through bal- anced Gaussian sampling based on historical class centroids and covariance matrices. This probabilistic routing mecha- nism enables context-awarc expert collaboration, allowing the model to preserve knowledge of many-shot classes while leveraging patterns learned from domains to enhance the representation of few-shot classes from previous tasks. It thus achieves a balance between knowledge sharing and resistance to forgetting.
Domain-Incremmenal Learning with Pre-Traine Trained Mod- els: The surge in pre-training methodologies has revo- lutionized DIL by offering robust initializations that en- hance feature transferability and generalization (Wang et al., 2022b;c;a; Kim et al., 2023; Smith et al., 2023). Recent DIL approaches that leverage pre-trained models (PTMs) primar- ily focus on visual prompt tuning (Jia et al., 2022), where the pre-trained feature extractor is frozen, and prompt pool is learned external knowledge. These prompts selec- tively applied to encode instance-specific or task-
Our contributions are threefold: (1) To the best of our knowl- edge, we are the first systematically study the impact of class imbalance in PTM-based DIL, addressing both intra- domain imbalance and cross-doman class distribution shifts. (2) We propose DCE, a framework that employs frequency-
2

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Addressing Imbalanced Nomain-Incremeent Learning through Dual- Dual-Balance Collaborative Experts
information. For instance, L,P (Wang et al., 2022c) em- ploys query-key matching mechanism for selecting ap- propriate prompts, while DualPrompt (Wang et al., 2022b) refines this strategy by jointly learning prompts specific to tasks and instances. In another approach, S-Prompts (Wang et al., 2022a) emphasizes Nomain-specific prompts, utilizing KNN-based retrieval for prompt selection. To streamline the prompt retrieval retrivell process, PrDmpP (Smith al., 2023) introduces an attention-driven weighted combination to re- place conventional selection mechanisms. Beyond prompt tuning, several alternative strategies have been proposed. These include directly constructing classifiers based on pre- trained features (Zhou et al., 2024a; McDonnell et al., 2023), merging models to integrate knowledge across tasks (Zhou et al., 2024b), and designing -ixxpess-ofEExperts architec- tures (Yu al., 2024) that dynamically select specialized experts. Among them, Yu et al. (2024) introduced a Mixture- of-Experts Adapter framework for vision-language models. In contrast, our method adopts a different expert structure and does not rely on a language model. Furthermore, none of these approaches explicitly address the class imbalance issue inherent in DIL, which our method is designed to handle.
{ {D), De,..., DDE.. where each Do (x )b represents the b-th training task. Here, Xd = {x;}== denotes the input instances, and 3b- = {y: J denotes the correspond- ing labels, with each input instance x; E IR' belonging to a class yi E y Let ng represent the number of samples belonging to class c in domain b, and ps(y) represent the class distribution in domain 6, where e E V. Unlike tradi- tional incremental learning scenarios where the label space may expand, DIL maintains fixed label space y through- out the learning process. However, the input distribution varies across domains introducing significant domain shifts that the model must adapt to without forgetting previously acquired knowledge. By decomposing the joint distribu- tion as pb(x, y) = po(y)ps (xly), we see that domain shifts include both concept drift (from Po (x(x)) and class distri- bution shifts (from po(y)) Current research often focuses the former while neglecting the latter. In real-worle see- narios, the difficulty of data collection for each class across different domains varies, resulting in discrepancies in their quantities. From the perspective of each individual domain, ps (y) may follow an imbalanced distribution, which we term intra-domain imbalance. From an inter-domain per- spective, the variation of pb(y) across domains manifests cross-domain class distribution shift. For simplicity, we define this setting as imbalanced DIL. and conduct our study within this framework.
learning: A lot of research has explored ilaal-imealancedd learning (Liu et al., 2019; Zhang et al., 2023b). Proposed approaches typically include strategies like re-balancing the data through over-sampling few-shot classes or under-sampling many-shot classes (Chawla et al., 2002; He et al.. 2008; Mingjian et al., 2022), adjusting or re- weighting loss functions (Cui al., 2019; Cao al., 2019; Menon et al., 2020; Ren et al., 2020; Ye et al., 2020), and leveraging various learning paradigms. These paradigms encompass transfer learning (Liu et al., 2019), metric learn- ing (Zhang et al., 2017), meta-learning (Shu et al., 2019), two-stage training (Kang et al., 2020), ensemble learn- ing (Wang et al.. 2020; Zhang et al., 2021), self-supervised learning (Yang& & Xu, 2020; et al., 2022) and knowledge distillation (Li et al., 2024). Compared with these works, we focus on the class imbalance learning problem in DIL, where new challenges need to be addressed.
3.2. Dilemmas of Current Methods
In this paper, we focus on PTM--based methods in the exemplar-free DIL setting. Generally, the model can be divided into two main components: the feature encoder and the linear classifier. The feature encoder f(x) R + Rd is initialized with the parameters 6g of a pre-trained Vision Transformer (ViT) (Dosovitskiy et al., 2020), as assumed in (Wang al., 2022b;c;a; Smith et al., 2023). Additionally, the feature encoder contains some adjustable parameters 01, such as prompts, adapters, or feature transformations, to help the model adapt to different domains.
In DIL, when encountering a new domain, the common approach to reduce catastrophic forgetting of previous do- mains while capturing discriminative features within the new domain is freeze the pre-trained weights and fine- tune the adjustable parameters 0 on the new domain's 's data to encode domain-speciin knowledge. Formally, the opti- mization objective is:
3. Imbalanced Domain-Incremental Learning
This section provides an overview of domain incremental learning, examines the challenges posed by class imbalance, and critiques existing incremental learning methods in the
context of imbalanced data.
min Σ (X.
e(h , 9) + Ces,
(I)
3.1. Background
where Dd is the dataset for the b-th domain, h is the classifier, i is the loss function, and Ls, is a regularization term for the adjustable parameters 0j. In DIL with PTMs, existing approaches can be categorized into two paradigms based on prompt learning strategies (Wang et al., 2022a):
learning (DIL) (Zhou et al., 2023a) is subfield of incremental learning where a model encounters a sequence of tasks, each associated with distinct domain. Formally, in DIL, the model encounters a sequence of tasks
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Addressing Imbalanced Domain-Inerement Learning through Dual-Baland Collaborative Experts
Shared prompt paradigm. Taking L,2P (Wang et al., 2022c) an example, it maintains learnable prompt pool as shared knowledge across domains. Specifically, domain- specific knowledge is encoded in parameterized prompt sets 01, while the prompt selection mechanism is guided by Co.. This paradigm adapts feature representations through retrieved prompts and progressively extends the classifier dimension with emerging tasks. The final prediction ad- justed using modulo operation over the label space size [ However, continuously updating knowledge within shared feature space inherently causes entanglement between old and new knowledge subspaces, leading to degraded class separability.
possess two key capabilities to address the challenges posed by intra-domain imbalance and cross-domain distribution shifts. (1) For intra-domain imbalance, the model should balance learning across different classes during training, reducing the bias towards many-shot classes. (2) For cross-domain class distribution shifts, the model must strike a balance between preserving prior knowledge and acquiring new information. Specifically, the model should retain the discriminative ability of well-learne' many- shot classes to prevent forgetting, while allowing few-shot classes to benefit from new task data and improve general- ization without being constrained by outdated knowledge.
Domain-specific prompt paradigm. Methods such as S- iPrompt (Wang et al., 2022a) learn independent sets of parameters 8f for each domain b. They establish cluster- based domain identification mechanism: extracting domain features via PTM, clustering them into domain prototypes, and performing hard assignment during inference by measur- ing similarity between test samples and prototypes. While this architecture mitigates knowledge interference through parameter isolation, its strictly segregated design faces crit- ical limitations the absence of explicit cross-domain knowledge sharing mechanisms may result in suboptimal generalization capability.
4. Methodology
The following sections detail our Dual-Balance Collabora- tive Experts (DCE) framework, which includes two training stages per domain, designed to address the limitations of both shared and domain domain-specific prompt paradigms. In the first stage, we construct frequency-aware expert networks to isolate task-specific knowledge, with each expert spe- cializing in different class frequency groups. This design mitigates intra-domain imbalance and alleviates forgetting due to shared feature spaces. In the second stage, we de- velop dynamic expert selector that assigns soft weights to the contributions of each expert. The selector is trained on balanced pseudo-features sampled from historical class statistics. This mechanism encourages knowledge sharing across tasks, enabling underrepresented classes from previ- ous domains to benefit from new task data, thereby improv- ing generalization without compromising stability.
While both paradigms suffer from class imbal- ance, their underlying mechanisms exhibit distinct character- istics. As shown in Figure 2, intra-domain class imbalance affects both paradigms similarly. For few-shot classes in the training domain b,, models tend to misclassify sam- ples as many-shot classes due to underfitting, resulting in significant accuracy disparities between frequency groups.
Discussions:
4.1. Frequency-Aware Experts
But cross-domain class distribution shift reveals a signifi- cant difference between shared prompt and domain-speciff prompt methods. Shared prompts maintain common prompt pool and feature space across tasks, which facil- itates knowledge transfer and improves few-shot class per- formance on earlier domains after training new tasks. How- ever, this shared space also increases conflicts between old and new tasks, resulting in more forgetting for many- shot classes, as shown in Figure 2(a). In contrast, domain- specific prompts isolate prompts and feature spaces for each task, effectively reducing forgetting for many-shot classes when the test sample's task is correctly identified. Never- theless, this isolation limits the transfer of knowledge to few-shot classes from new tasks, thereby restricting their generalization, as illustrated in Figure 2(b). In short, al- though shared prompts enhance few-shot transfer, they carry a higher risk of forgetting many-shot classes, while domain- specific prompts reduce forgetting for many-shot classes at the expense of weaker few-shot generalization.
In imbalanced DIL, intra- dorain class imbalance leads to excessive bias toward many-shot classes while insufficient representation learning for few-shot classes. To address this, we propose multi-expert collaborative architecture that constructs specialized expert networks for different fre- quency classes through differentiated training strategies. As shown in Figure 3, we deploy three parallel expert modules downstream of the PTM, denoted as eb, 6b+1, and 66+2- targeting hany-shot-biased balanced, and few-shot-biased learning respectively. Each expert consists of an MLP and dedicated classifier.
For the first expert Eb. we employ the standard softmax cross-entrops loss:
exp(x:) fcE = - -log ΣjEDy exp(u))
where w² = es (0()). This loss assumes distributional alignment between training and test data, which is violated in class- -taa-iimalennced settings. As a result, it tends to favor frequent classes
Thus, we believe that in imbalanced DIL, the model should
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Addressing Imbalanced Domain-Incremenn Incremental Learning through Dual-Balane Collaborative Experts
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Figure 3. The overall pipeline of our proposed DCE, consists of two-stage training process: frequency-aware experts training and dynamic expert selector training. In the first stage, each expert is trained independently with its own loss function, where the visual prompt is learned in the first task and frozen thereafter. In the second stage, compute class wise means and covariances using the frozen feature extractor, then perform Gaussian sampling to construct synthetic feature set, which is used to train the expert selector.
To address this, the second expert 6b411 uses the balanced softmax loss (Ren et al., 2020), which corrects the bias by incorporating class frequency priors from the imbalanced training distribution pb(y) 1
samples with unknown domain affiliation. While these ex- perts alleviate catastrophic forgetting by isolating conflicting knowledge subspaces, naive expert selection remains prob- lematic. For example, domain-specific approaches such as S-iPrompt (Wang et al., 2022a) rely on hard assignment strategy, matching each test sample to a single domain ex- pert based on feature similarity to pre-computed domain prototypes, limiting flexibility and adaptability as the expert set grows. This static strategy is insufficient in the pres- ence of cross-domain label distribution shifts, where newly trained experts may offer stronger representations for few- shot classes from previous domains. To address this, we propose a dynamic expert selection mechanism that evolves with the expanding expert pool, enabling adaptive fusion of cross- cross-domain knowledge.
exp(x - log
Bal = - log Ejeny exp(u3 | log
(3)
Different from fcr that prioritizes many-shot classes, Bai courages balanced predictions by compensating for skewed label frequencies. we further propose an inverse distribu- tion loss Rovv for the third expert E6+2- This loss inverts the training distribution to emphasize few-shot classes, us- ing normalized inverse frequency distribution (v()) ( /// / Therefore have:
(pl
exp(1 3/ log p log p3)
We MLP s(-) Rdd R the dynamic expert selector task b. Given sample the selector takes the feature (xx) as input and outputs a weight vector w [w,] where each w; denotes the importance of expert 6;. Here, 36 corresponds to the total number experts accumulated up to task
&Rev = - log Σ EjE exp(v 3 log p/ log
exp(v " + 2oggp")
lo Σje(y exp(1 + log
The proof of Equation (4) is provided in Appendix A. This loss induces a stronger focus on few-shot classes, providing complementary behavior to eb.
To train the expert selector, first construct task-relevant and stable feature space using the frozen parameters of the pre-trained model. Specifically, during the training of the first task's experts, we employ Visual Prompt Tuning (VPT) (Jia et al., 2022) on the encoder 6, where the back- bone parameters O, are kept frozen and only the prompt parameters 0 are optimized to obtain a stable and DIL.- aware feature space. In subsequent tasks, both On and 0, frozen to maintain feature consistency across tasks.
During training, each expert is optimized independently using its designated loss. The overall training objective for expert modules is the sum of all three:
eexp
fcE + laal ERev-
(5)
4.2. Dynamic Expert Selector
After training task-specific experts, the central challenge lies in selecting an optimal combination of experts for test
Following the observation from Zhang et al. (2023a) that PTMs typically yield unimodal feature distributions per
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Addressing Imbalanced Domain- Incremental Learning through Dual- Collaborative Experts
class, we model each class as a Gaussian distribution. Dur- ing DII., we continually collect and update feature statis- ties across domains. After training the experts for do- main b, we compute and store class-wise feature statistics G. = [(4, }. where nng and bi are the empirical mean and covariance of class c. These are then merged into a global statistical repository G = U Gú which incremen- tally captures the evolving cross-domain feature landscape.
Algorithm Incremental Training of DCE
Input: Incremental datasets: {D), De,... DBJ, Pre-
trained embedding: (o(z))
Output: Incrementally trained model for to B do
Get the incremental training set D6 if 6 then Jointly train prompt 0, and experts e2, eg by lexp in Equation (5).
During the optimization of the expert selector, cross-domain knowledge transfer is facilitated by sampling features from Gaussian distributions. For each domain-class pair (b, c) in G, Gaussian sampling is performed to generate K synthetic features, forming the synthetic dataset:
else
Train experts eb, 6b+1, 6b+2 by fexp in Equation (5).
end if
Compute per-class statistics Gj of D'.
Update global statistics: G < G U Go.
D D = UU ((x,)) ~ N(af D.E
Gaussian sampling on G to construct D via Equa-
(6)
tion (6).
==1==
Train selector on δ using feetect in Equation (7). end for
Importantly, the value of K is kept uniform across all domain-class pairs, ensuring balanced knowledge transfer across imbalanced classes and domains. The expert selector 8 is then updated using:
for an input x, the extracted feature 0(x) is passed to all experts. Their outputs are then weighted and fused by the expert selector s(((x)) to produce the final prediction.
36 Σ
1 D
Seeeect
LCE
(x); (x), y/
(7)
(
5. Experiment
However, estimating the covariance matrices in imbalanced DIL poses practical challenges. For classes with few sam- ples, the covariance estimation becomes unreliable. To address this, we adopt Oracle Approximating Shrinkage (OAS) (Chen et al., 2010), which introduces shrinkage mechanism to produce more stable estimates. Furthermore, with the covariance matrix having dimensions of d x d, storing a separate covariance matrix for each class across different domains would require substantial storage space. To reduce storage costs, we average the class-specific covari- ances within each domain, resulting in a single domain-level covariance matrix. More detail can be found in Appendix B.
In this section, we conduct experiments on benchmark datasets to compare to existing state-of-the-an methods, and provide detailed analysis of the experimental results.
5.1. Experimental Setup
Dataset: Following benchmark settings in PTM-based DIL. (Wang al., 2022b;c; Smith al., 2023), we eval- uate four widely-used DIL benchmark datasets: Officc- Womee(eenattesaat et al., 2017) (4 domains), Domain- Net(Peng et al., 2019) (6 domains), Maltoni, 2017) (11 domains), and HoDD-Hard(Li et al., 2023) (5 domains). It is worth noting that the Office-Home and DomainNet datasets are inherently highly imbalanced. Following Yang al. (2022), we retain the original imbal- anced training distributions for Office- and Domain- Net, while constructing balanced test sets by equalizing the number of samples per class. For these two datasets, we categorize classes in each task into many-shot, medium- shot, and few-shot groups using thresholds 20 and 60 for Office-Home, and 20 and 100 for DomainNet. For CORe50, we follow Cui al. (2019) and apply distinct class im- balance ratios per domain to create imbalanced training sets. Similarly, in CDDB-Hard, we systematically adjust the positive-to-negative sample ratios to induce per-domain class imbalance. Note that the test set of CORe50 consists of three outdoor sessions that are drawn from a distribution different from the per-task training domains. Additionally,
(oRe50((Lomonac &
4.3. Summary of DCE
The proposed DCE is illustrated in Figure 3, and sum- marized in Algorithm 1. Each task involves two stages: frequency-aware expert training and dynamic expert selec- tor training. In the first stage, we independently train each expert using its corresponding loss function. The prompt pa- rameters 8 are optimized via VPT during the first task and kept frozen for all subsequent tasks to ensure consistency. In the second stage, compute and store the class wise mean and covariance of the features extracted by the frozen pre-trained model. These statistics are used to synthesize representative features via multivariate Gaussian sampling. The resulting synthetic feature set Đ is then used to train the expert selector through the loss fseles- During inference,
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Addressing Imbalanced Domain-Incremenne Incremental Learning through Dual- Hual-Balance Collaborative Experts
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Figure 4. Class distribution of training samples in DomainNet and CORe50. DomainNet is inherently imbalanced; hence, we construct a class-balanced test set for evaluation. Dashed lines denote the thresholds for many-shot, medium-shot, and few-shot class divisions. For 50 imbalanced training sets are manually created.
Table Average and last performance of different methods among five task orders. The best performance is shown in bold. All methods are implemented with ViT-B/16 INIK. Methods with indicate implemented with exemplars (10 class). DomainNet
Office-Home
Method
A
Ag
Ammay
Amat
Ate
A
As
Amany
Amed
Atew
iCaRL (Rebuffi et al., 2017)
83.2+sa
83,5+a7
90.5
82.8+a9
66.6+3.1
58.4+5a
55.9+17
58,0+13
48.3+
36.5+a5
MEMO" (Zhou et al., 2023b)
78.7
19.
86.421a
78.1213
60.5
57.8267
56.421a
58.321a
50.1
38.21aa
SimpleCII (Zhou et al., 2024a)
75.2
76.2190
81,61a0
74.7
73.7
41.1208
40.6200
41.5am0
40.82nn
30.8100
RanPAC (MeDonnell et al., 2023)
83.4+ss
83,3+aa
89.1+as
82.1+a7
72.5+36
57.8+53
56.1+as
58,3+00
52.5+04
40.0+aa
L2P (Wang et al., 2022c)
78.7+44
80.5+a6
86,1+ap
79.0+1u
73.7+39
48.5+aa
45,2+23
46.7+23
42,0+1s
37.3+0a
DualPrompt (Wang ul., 2022b)
77.2+3.
79.1
84.3
77.3+07
70.6#23
52,3
50.9
52.5+5m
44.7+as
37.5+24
MODA-Prompt (Smith et al., 2023)
82.4asx
83.3as3
88.7a93
81.94s6
73.2
47.6261
15.1
46.6213
42.2a1a
38.2214
S-iPrompt (Wang et al., 2022a)
81.4233
80.8an3
88.4211
79.1
66.0
59.02ex
57.9202
61.3am0
47.3207
31.52m0
DCE
84.6:30
84.4
88.7
83.2:a3
79.4:15
64.3
63.5
65.2:04
58.6:a5
50.8
Table 2. Average and last performance of different methods among five task orders. The best performance is shown in bold. All methods are implemented with ViT-B/16 INIK. Methods with indicate implemented with exemplars (10 per class).
against the state-of-the-art CIL/DIL methods. (1) exemplar- based methods, including iCaRL (Rebuffi et al., 2017) and MEMO (Zhou et al., 2023b), and (2) state-of-the- art exemplar-free PTM-based domain-ineremmntall learning methods, such as SimpleCIL (Zhou et al., 2024a), Ran- PAC (McDonnell et al.. 2023), L2P (Wang et al., 2022c), DualPrompt (Wang et al., 2022b), CODA-Prompt (Smith et al., 2023), and S-iPrompt (Wang et al., 2022a). To ensure fair comparison, all methods are implemented using the same pre-trained backbone network.
CORe50
-DDB-Hard
Method
A
Ab
A
Ab
iCaRLI
71.0#37
76.0#27
57.5
54.4_99
MEMO
66.0127
68.2217
66.0127
68.2_17
SimpleCIL
62.5
67.2+u0
65.5
64.1-17
RanPAC
+6.7
78.4
61.7444
62.5-
1.2P
12.3
81.7=as
67.3+55
65.0=65
Implementation details: We deploy the experiments using PyTorch (Paszke et al., 2019) and Pilot (Sun et al., 2025) on NVIDIA 4090. Following (Wang et al., 2022c; Zhou et al., 2024c), we consider two typical pre-trained weights, i.e., VVT-B/16-IN2IK K and ViT-B/16-INIK, Both are pre-trained with ImageNet2IK (Russakovsky et al., 2015), while the latter is further finetuned on ImageNetIK. We optimize DCE using SGD optimizer with a batch size of 128 for 20 or 30 epochs. The learning rate is set to 0.001. We select 10 exemplars per class for exemplar-based methods using herding (Welling, 2009) algorithm. More detail can be found in Appendix F.
DualPrompt
71.0145
77.7110
66.9.5s
65.6
CODA-Prompt
72.8
81.4
67. 2...
66.6=2x
S-iPrompt
62.712a
65.81n9
64.2158
63.4_28
DCE
80.1
84.8
74.6165
71.8-42
each task in CDDB-Hard is a binary classification prob- lem. As a result, we do not perform many/medium/few-she categorization on CORe50 and CDDB-Hard. We evalu- ate each setting using five randomized domain orders to ensure robustness, with detailed configurations provided in Appendix F. The training distributions of DomainNet and CORe50 are illustrated in Figure 4.
Performance Mcasure: Following (Wang et al., 2022c;a), we denote the accuracy across all seen domains after learn-
Comparison methods: We compare the proposed DCE
7

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Addressing Imbalanced Somain-Inerementa Learning through Dual-Balance Collaborative Experts
70
5.70
4.22
A
ThaiPhorpe
Deathonge
Dailhwoye
NENO
CODN-Paarp
MEMO
CODA-Pruurp S-iterrpt
MENO
CUDNN-raapp
StrpatiL
SiShampe
Strplacte
SampatiL
RastAC
DCE
DCS
REStAC
DCE
12P
1:2
12P
Donsin
Densin
Demsin
(a) DomainNet VIT- INIK
(b) DomainNet VIE-B/I6 INZIK
(c) DemainNet VIT- INIK WBCE
3.48
1.58
30
71
001
-00
MENO
KORL
CUDA-Paapp
Triihorrge
30
MEMO
ICaRI
CODx-Pruss
MENO
ICRL
+
CODN-Paarp
DuilPhorpe
S.Phoret
STT
5iTherg
SirpkCIL
S.Phrgge
DCE
10
CORe50 VIT-B/16
(e) CORe50 VIE-B/16
(D) CORe50 VIT-B/16
Figure 5. Incremental performance of different methods with the same pre-trained model. We report the performance gap after the last incremental stage hetween DCE and the runner-up method at the end of the line.
ing the b-th task as As. For comparison, we primarily con- sider AB (the accuracy after completing the final stage) and Á = B Σ Ab (the average accuracy across all in- cremental stages). Additionally, we evaluate the model's performance across categories with different frequencies: Amany. Amod. and Atew represent the accuracy on many-shot, medium-shot, and few-shot classes, respectively, at the end of training.
ods with the balanced softmax loss l Since these methods are not originally designed for this objective, some of them suffer performance degradation. Nonetheless, as shown in Figure 5(c) and (f), DCE maintains a clear advantage and continues to outperform the baselines under this challenging setting.
5.3. Further Analysis
Class Performance Drift: In DIL., the conventional for- getting measure is less applicable because class accuracy can increase over time, especially for few-shot classes. To address this, we introduce new metric, Class Performance Drift (CPD), which quantifies the accuracy change of each class during training. Specifically, for a class e domain b, let of, denote its accuracy immediately after training on domain b, and a'g denote its accuracy after completing train- ing on the final domain B. The CPD is then defined as CPDş ag ag. which reflects the performance drift of class from domain b. A positive CPD indicates perfor- mance degradation, while a negative value signifies perfor- mance improvement.
5.2. Main Results
We present comprehensive performance statistics (mean + std) across five task sequences in Table I and Table 2, cover- ing four benchmark datasets. From these results, we observe that DCE consistently outperforms comparison methods. Compared to the second-best method, DCE maintains a stable lead on both AB and Å, demonstrating its robustness across different evaluation metrics. In Table 1, we also re- port the final model's accuracy for many-shot, medium-shot, and few-shot classes separately. Benefiting from the dual- balance approach we adopt, our method shows significant performance improvement on few-shot classes.
In addition, Figure 5(a) and (d) illustrate performance pro- gression across tasks on DomainNet and CORe50, while Figure 5(b) and (e) show training dynamics using ViT- B/16 pre-trained on ImageNet-21K (IN21K). Our approach demonstrates consistently stable and superior performance, validating its robustness across tasks and compatibility with different pre-trained backbones.
In Figure 6, we report the mean and variance of CPD on DomainNet across five runs, covering many-shot, medium- shot, few-shot, and all classes. The results are consistent with our earlier analysis. Shared prompt methods (e.g.. 1.2P, DualPrompt, CODA-Prompt) show higher CPD for many-shot and medium- classes, indicating more severe forgetting, but lower CPD for few-shot classes, suggesting performance gains from new domain data. In comparison,
To ensure a fair comparison, we re-implement baseline meth-
8

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Addressing Imbalanced Comain-Innremmeen Learning through Dual-Balance Collaborative Experts
iCaRL
MEMO
SimpleCII
RanPAC
L2P
DealPrompt
CODA-Prompt
S-iPrompt
DCE(ours)
12.5
20
10.0
15
7.5
15
10
10
-10
2.5
T
5
0.0
-15
0
5.0
Many-shot
Medium-shot
Few-shot
Figure 6. Class Performance Drift (lower is better) of different methods on DomainNet dataset among five task orders. DCE demonstrates a balance between reducing forgetting of many-shot classes and improving the performance of few-shot classes.
improves, particularly on medium- and few-shot classes. This confirms the effectiveness of our multi-expert strategy. However, the performance gain becomes marginal when adding more than three experts. Therefore, our method adopts a three-expert configuration to strike a balance be- tween effectiveness and efficiency, More discussion can be found in Appendix E.
1000
Ape
Avai
The right part of Figure 7 presents the training and inference costs of different meth- ods. All experiments are conducted on an RTX 3090 GPU, and we report the average per-batch time under the same batch size. For DCE, the average per-batch training time after the first task is computed by summing the durations of both the first and second training stages. Since DCE updates only expert parameters after the first task, the gradi- ent does not propagate through the feature encoder, which eliminates the need to construct a computation graph over it. Additionally, unlike methods such as L.PP and DualPrompt that require two forward passes through the encoder for both training and inference, DCE requires only single forward pass. As result, method significantly reduces computational cost in both phases.
Computational Efficiency:
TEppet
3iapetaDCTi
Tapanss
Eapers
Figure Left: On the DomainNet datasct, the performance of the model with different numbers of experts task. Right: The per-batch training and inference time of different methods on DomainNet dataset.
domain-specific prompt methods (e.g., S-iPrompt) achieve lower CPD for many-shot and medium-shot classes, indi- cating better knowledge retention, but exhibit higher CPD for few-shot classes, implying less benefit from new tasks. DCE provides more balanced outcome, maintaining mod- erate CPD across all class types. reduces forgetting in common classes while still improving the performance of rare ones. In terms of overall CPD, method ranks sec- ond only to SimpleCII, which does not involve training and relies solely on class prototypes. This demonstrates the effectiveness of our dual-balance strategy in balancing knowledge retention and adaptation.
6. Conclusion
In this work, we present DCE a novel framework for Domain--nnremeenal Learning under class-imbalanced con- ditions. DCE addresses two fundamental challenges: intra- domain class imbalance and tross-domain class distribu- tion shifts, both of which frequently occur in real-world scenarios. By introducing frequency- collaborative ex- perts trained with specialized objectives, our method effec- tively balances the representation many-shot and fhw-shot classes during domain Spmeffi learning. Furthermore, the dynamic expert selector, guided by pseudo-features synthe- sized from historical statisties, enables adaptive knowledge transfer across domains while mitigating forgetting. Exten- sive experiments on four benchmark datasets validate the effectiveness and efficiency of DCE.
The left part of Figure presents systematic evaluation of the impact of using dif- ferent numbers of experts. We compare the model's perfor- mance under four configurations: We compare the model's 's performance under four configurations: (1) a single expert trained with fcE; (2) two experts trained with feE and Baat (3) three experts with an additional ÉRev (i.e., our proposed DCE configuration), and (4) four experts, where an extra exp(1; +33 log))))
Effectiveness of Multiple Experts:
loss l log Exeryi exp(e; [og is introduced in ad- dition to the previous three losses. The results show that as the number of experts increases, overall model performance

---

Addressing Imbalanced Iemmiin-nerrmmnntal Learning through Dual-Balance Collaborative Experts
Acknowledgements
Heigold, G., Gelly, S., et al. An image is worth 16x 16 words: Transformers for image recognition at scale. In ICLR, 2020.
This work partially supported by National Science and Technology Major Project (2022ZD01 14805), NSFC (62376118, 62250069), Fundamental Research Funds for the Central Universities (14380021, 2024300373), the AI & Al for Science Project of Nanjing University, Postgrad- uate Research & Practice Innovation Program of Jiangsu Province, Collaborative Innovation Center of Novel Soft- ware Technology and Industrialization.
Floridi, L. and Chiriatti, M. Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4):681-62) 2020. Gama, J., Zliobaité, I,, Bifet, A., Pechenizkiy, M., and Bouchachia, A. A survey on concept drift adaptation. ACM computing surveys, 46(4):1-37, 2014.
Impact Statement
Han, X., Zhang, Z.. Ding, N., Gu, Y., Liu, X., Huo, Y.,
Qiu, J., Yao, Y., Zhang, A., Zhang, L.., et al. Pre-trained models: Past, present and future. Al Open, 2:225-2500 2021.
This paper presents work whose goal is advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
He, H., Bai, Y., Garcia, E. A., and Li, Adasyn: Adap- tive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks, 2008
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Addressing Imbalanced
Learning through Collaborative Experts
A. Proof of Equation (4)
Let the target class probability distribution be p(y) [ and the source class probability distribution be p(y) {-)) Assuming identical class-conditional probabilitiess p(aly) = p(xly)) between source and target domains, we derive
through Bayes' theorem:
p(yxx) & P( x)p())
(8)
p(y)
To ensure p(y(x) forms a valid probability distribution, we normalize it follows:
p x rly) Pl
p(y1x)
p(y|z)
(9)
Ejey p(jxx)
Σ EEEY ) PG) P6)
exp(xy)
Eicy explec)
pby
(10)
Ejey
exp(11)) Eiey exp(Ps)
exp(vyy)
poN
(11)
ΣjEy exp(v;) Pi
Pb
exp (By + log - log p3g
(12)
Ejeyesy exp (1j + log pí log pb)
Substituting the target class probability distribution
1/pb Eiel>i 1/rb
(13)
into Equation (12) yields the final form of Ree in Equation (4):
exp (vy log pg)
fRev logp(ulx)= log
(14)
Ejey exp U/ + 2 log p/
B. Estimate of covariance matrices
In our imbalanced DIL framework, we employ Oracle Approximating Shrinkage (OAS) (Chen et al., 2010) to estimate class- conditional covariance matrices across sequential domains. This regularization technique addresses the small-sample-size problem in individual classes.
For class in domain with samples {x, )- the regularized covariance estimate Ee,d is computed as:
Eod (1 - p)) + pripriors
(15)
where Semp = I Σi= (x; - (x) Te) is the empirical covariance, Epnir tr tr(Seep)/d / Ta denotes the spherical shrinkage target, and p is the OAS shrinkage coefficient given by:
(1 ()) +Ir -tr(S2 + ur( 1r(Semp))
(16)
(n + 1 - 3). (tr(5 (r(S2 Str(Eeeng)
Given the memory constraints in incremental learning, our implementation calculates OAS-regularized covariance matrices only when a class contains sufficient samples (n 2 10) within domain. The resulting matrices are aggregated through seement-wise averaging to construct a shared covariance structure, effectively balancing computational efficiency with statistical reliability.
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Addressing Imbalanced Domain- Incremental Learning through Dual-Balane Collaborative Experts
C. More Comparative Experiments
We further conduct comparative experiments with other incremental learning methods on the Office-Home and DomainNet datasets. ROW (Kim al., 2023) a replay-based incremental learning method that, similar S-iPrompt, learns a set of task-specific parameters for each task. However, it selects appropriate parameters through an out-of-distribution detection mechanism. DUCT (Zhou al., 2024b) is recent DIL method that addresses domain shifts by continuously merging task-specific parameters using a model merging strategy. Although these methods perform well in traditional incremental learning settings, they fail to adapt to the imbalanced DIL scenario we target. In contrast, our proposed method, DCE, achieves the best performance.
Table Average and last performance of different methods among five task orders. The best performance is shown in bold, All methods are implemented with ViT-B/16 INIK.
Office-Home
DomainNet
Method
A
Ab
A
Ab
ROW (Kim et al., 2023)
81.1 +39
82.2=04
58.3
57.1 +0.
DUCT (Zhou et al., 2024b)
64.7=18
58.8=37
26.6212
18. 18.0260
DCE
84.5=19
84.4-m.1
64.2=s.
63.4
D. Detailed Experimental Results
In the main manuscript, we conducted experiments on benchmark datasets using five distinct task sequences and reported the average performance. To provide comprehensive insights, we present the task-specific performance for each sequence in Table 4, Table 5, Table 6, Table 7, Table 8. with full details of the task sequences documented in Appendix F.3. Table 4, Average and last performance of different methods with the 1st task order in Section F.3. The best performance is shown in bold. All methods are implemented with ViT-B/16 INIK. Methods with indicate implementations with exemplars (10 per class).
Office-Home
DomainNet
CORe50
CDDB-Han Hard
Method
A
A
Á
Ap
Á
An
An
! (Rebufti et al., 2017)
80.2
84.5
58.2
58.2
71.8
75.8
50.7
51.6
MEMO (Zhou et al., 2023b)
74.2
79.4
57.2
58.1
64.1
66.2
57.6
64.3
SimpleCIL (Zhou et al., 2024a)
71.8
76.2
38.9
40.6
62.2
67.2
62.9
63.3
RanPAC (MeDonnell et al., 2023)
78.6
83.1
55.8
56.3
76.8
78.1
57.2
60.1
L2P (Wang al., 2022c)
74.5
80.5
45.9
47.5
71.7
81.6
60.1
56.6
DualPrompt (Wang et al., 2022b)
72.4
78.7
44.8
49.4
68.8
78.4
65.0
62.0
CODA-Prompt (Smith et al., 2023)
78.6
83.1
45.8
46.4
71.5
82.0
60.8
65.5
S S-iPrompt (Wang et al., 2022a)
78.6
81.1
56.7
58.1 1
63.9
66.3
59.0
67.7
DCE
81.7
84.5
62.2
63.9
80.8
84.8
70.2
72.3
Table Average and last performance different methods with 2rd task order Section F.3, The best performance shown bold,
All methods are implemented with ViT-B/16 INIK. Methods with indicate implementations with exemplars (10 per class).
Office-Home
DomainNet
CORe50
EDDB-Hard
Method
A
Ag
Ag
A
Ab
A
Ab
iCaRLi (Rebuffi et al., 2017)
80.7
83.2
49.6
56.9
73.0
78.0
67.4
48.1
MEMO! (Zhou et al., 2023b)
78.5
80.2
46.4
55.3
66.0
68.4
84.6
75.0
SimpleCIL. (Zhou et al., 2024a)
68.8
76.2
34.2
40.6
64.8
67.2
66.8
63.3
RanPAC (MeDonnell al., 2023)
78.8
83.7
50.0
56.4
78.4
80.0
66.7
66.1
L2P (Wang ct al., 2022c)
74.8
81.2
40.1
42.3
73.1
81.1
67.9
61.3
DualPrompt (Wang et al.. 2022b)
71.9
79.6
41.5
45.4
70.2
78.2
63.8
71.9
CODA-Prompy (Smith et al., 2023)
78.8
83.7
40.3
44.2
73.8
81.7
72.8
64.7
S- S-iPrompt (Wang et al., 2022a)
77.8
80.7
48.5
57.8
64.9
66.4
68.9
62.1
DCE
81.5
84.8
55.0
63.2
79.8
84.7
81.0
75.4
14

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Addressing Imbalanced Domain- Incremental Learning through Dual-Balanc Collaborative Experts
Table Average and last performance of different methods with the 3rd task order in Section F.3. The best performance shown in bold. All methods are implemented with VIT-B/I B/16 INIK. Methods with i indicate implementations with exemplars (10 class).
Office-
DomainNet
CORe50
CDDB-
Method
A
Ab
Ag
A
Ab
A
Ab
iCaRL' (Rebuffi et al., 2017)
86.2
83.9
60.1
54.1
73.2
77.7
50.0
51.1
MEMO (Zhou et al., 2023b)
81.1
77.2
60.8
54,9
69.8
70.7
51.3
53.5
SimpleCIL (Zhou et al., 2024a)
76.7
76.2
44.5
40.6
61.4
67.2
66.3
63.3
RanPAC (McDonnell al., 20233
85.0
82.8
59.2
55.0
75.7
77.7
59.2
59.3
L2P (Wang ct al., 2022c)
80.8
79.5
49.6
44,5
73.8
82.5
70.0
66.1
DualPrompt (Wang al., 2022b)
79.2
78.6
51.0
49.1
68.6
77.5
66.1
68.8
CODA-Prompl (Smith et al., 2023)
85.0
82.8
47.3
44.5
72.1
80.1
69.1
66.4
S-iPrompt (Wang et al., 2022a)
83.9
80.9
63.0
57.8
63.2
66.1
60.3
60.9
DCE
86.4
84.5
66.5
63.1
79.1
85.3
70.9
66.4
Table Average and last performance of different methods with the 4th task order Scction F.3. The best performance shown in bold, All methods are implemented with ViT-B/16 INIK. Methods with indicate implementations with exemplars (10 class).
Office-Honee DomainNet
CORe50
CDDB
Method
Ab
A
A
Ab
A
Ab
iCaRL (Rebuffi et al., 2017)
86.9
83.3
60.9
54.6
72.8
77.1
69.1
71.9
MEMO (Zhou al., 2023b)
83.3
78.7
61.8
57.2
67.2
67.1
83.5
83.9
SimpleCIL (Zhou et al., 2024a)
81.0
76.2
51.0
40.6
63.2
67.2
69.8
67.1
RanPAC (McDonnell al., 2023)
87.2
83.3
65.1
56.5
75.1
76.1
65.9
62.9
L2P (Wang et al., 2022c)
84.4
80.8
58.7
47.6
72.7
82.0
74.1
73.7
DualPrompt (Wang al., 2022b)
84.4
79.9
54.6
79.1
78.2
69.9
72.4
CODA-Promen (Smith et al., 2023)
87.2
83.3
57.2
46.4
74.3
82.7
75.2
71.5
S- S-iPrompt (Wang et al., 2022a)
85.6
80.5
65.9
58.1
62.2
65.7
72.0
61.6
DCE
88.5
84.3
70.3
63.0
80.7
84.5
82.4
76.1
Table Average and last performance different methods with 5th task order Section F.3. The best performance shown bold.
All methods are implemented with ViT-B/16 INIK. Methods with indicate implementations with exemplars (10 class).
Office-Home
DomainNet
CORe50
CDDB-Hart
Method
A
Ap
Ap
A
Ap
Ab
iCaRL (Rebuffi et al., 2017)
82.0
82.9
63.4
55.9
64.4
71.5
50.0
49.2
MEMO! (Zhou al., 2023b)
76.3
79.9
62.9
56.6
62.7
68.5
56.5
53.1
SimpleCIL (Zhou et al., 2024a)
77.7
76.2
36.7
40.6
60.8
67.2
61.9
63.3
RanPAC (MeDonnell al., 2023)
82.4
83.5
58.9
56.6
77.4
80.1
59.2
64.0
L2P (Wang et al., 2022c)
79.0
80.7
48.3
44.2
70.3
81.4
64.5
67.5
DualPrompt (Wang et al.. 2022b)
78.2
78.9
55.9
68.6
76.1
63.5
59.7
CODA-Promph (Smith et al., 2023)
82,4
83.5
47.4
44.1
72.3
80.4
61.7
64.8
S- iPrrmpp (Wang et al., 2022a)
81.4
80.9
61.2
57.9
59.2
64.3
60.8
64.7
DCE
84.9
84.4
67.6
64.2
80.2
85.0
68.7
68.9
E. Ablation on the Effectiveness of Multi-Expert Design
Figure 8 presents the model performance across differentt class frequencies under varying expert configurations. With a single expert, fgr performs best on many-shot classes but poorly on few-shot classes; conversely, &eo excels on few-shot classes while underperforming on many-shot ones. laat achieves relatively balanced results. When two experts are used, the same trend holds: fcE + Bat favors many-shot classes, Bat + Rew benefits few-shot classes, and fcE Rew provides
15

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Addressing Imbalanced Comain--nerementa Learning through Dual-Balance Collaborative Experts
more balanced trade-off. These results indicate that better Jlas-nyry balance typically leads to improved overall performance. The three-expert design in our DCE framework can be integrating the strengths of Bal and lge &ees yielding superior results compared to single or dual expert settings.
70
60
Accuracy
Accuracy
Accuracy
50
40
Eper
lcs Feol
Iep feo
DCE
a Experts
Eaal
ic feer
fow faoe
DCB
4 Exports
fan
Epe fee
DCE
4 Exports
tes
Los
Exp
(a) Performance Comparison on Many-Shot Data
(b) Performance Comparison on Medium-Shor Data
(6) Performance Comparison on Few-Shot Data
Figure Performance comparison under different expert configurations. Accuracy on (a) many-shot, (b) medium-shot, and (c) few-shot classes is reported DomainNet using different expert setups.
F. Detailed Experimental Setup
F.1. Experimental Datasets
Office-Home (Venkateswara al., 2017) provides controlled testhed for gradual domain shifts across four modalities: Art (abstract representations), Clipart (stylized graphics), Product (isolated objects), and Real- World (natural images). Its 15.5K images spanning 65 categories exhibit progressive distribution shifts, making it ideal for studying catastrophic forgetting under moderate-scale incremental learning scenarios.
DomainNet (Peng al., 2019) is the largest multi-domain benchmark for cross-domain continual learning, comprising 345 fine-grained object categories across six distinct visual domains: (1) Clipart vector graphic illustrations, (2) Real- natural scene photographs, (3) Sketch freehand drawings. (4) Infograph information graphics with contextual elements, (5) Painting artistic renderings, and (6) Quickdraw time- cime-constrained doodles. Following standard protocols (Zhou et al., 2023b), we employ the noise-filtered "Cleaned" version containing 0.6M images, with domain shifts characterized by both style and contextual variations.
CORe50 (Lomonaco & Maltoni, 2017) evaluates continual learning under environmental dynamics through 11 acquisition sessions (8 indoor/3 outdoor) capturing 50 household objects under varying viewpoints and illumination. Its unique RGB-D temporal sequences (300 frames/objecctsessionn) enable testing of both spatial and temporal feature stability.
CDDB-Hard (Li et al., 2023) presents continual deepfake detection challenge featuring 12 evolving forgery techniques (e.g., GAN/Neural--exture variants) across 5 tasks. The "Hard" track (Wang et al., 2022a) introduces maximum task confusion through overlapping manipulation artifacts and progressive quality improvements, simulating deception evolution. The benchmark contains 100K real/fake image pairs with temporal metadata, requiring models to maintain detection capability while adapting to emerging forgery paradigms.
F.2. Dataset Construction.
Among the four datasets mentioned above, Office-Home and DomainNet are inherently class-imbalanced. Unlike the common practice of splitting training and testing sets proportionally. in class-imbalanced learning, imbalanced testing set is required to evaluate the algorithms. Therefore, following the setting of Yang et al. (2022), we randomly select 10 samples from each class to construct the testing set and adopt (20, 60) and (20, 100) training samples as the demarcation for many-shot, medium-shot, and few-shot classes on Office-Home and DomainNet datasets respectively.
In contrast, the CORe50 and CDDB- Hard datasets are originally balanced, necessitating manual construction of imbalanced training sets. For CORe50, we follow the setting in Cui al. (2019), creating different imbalanced tasks based on varying
16

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Addressing Imbalanced Domain-Increment Incremental Learning through Dual-Balance Collaborative Experts
AMt
Clipert
St
Gauges
200
Integmaph
Closn
580
Wild
Prodect
Culcadruw
430
55
201
Sai
511
5>
Clans
Class
10
Clos 1D
Figure The class distribution training samples in the four datasets. Office-Home DomainNet datasets are inherently imbalanced, so we constructed a class-balanced test set for evaluation. The dashed lines indicate the thresholds for the division of many-shot, medium-shot, and few-shot classes. For CORe50 and CDDB-Hard, we manually constructed imbalanced training sets.
imbalance ratios = -Naxx/ where Nmax represents the number samples in the majority class and Nmia represents the number of samples in the minority class. Among the training tasks, are assigned an imbalance ratio 100, and the other are assigned ratio of 50.
For the CDDB-Hard dataset, construct class-imbalanced data by specifying the number of positive and negative samples follows:
gaugan: 3000 negatives and 300 positives
biggan: 240 negatives and 1200 positives
wild: 310 negatives and 3115 positives
whichfaceisreal:: 600 negatives and positives
san: 130 negatives and 130 positives
The number of training samples across all datasets is illustrated in Figure
F.3. Task Sequences
learning scenarios, algorithmic performance exhibits sensitivity to domain ordering. To rigorously assess this factor, generated five randomized permutations of domain sequences through stratified sampling, which systematically analyzed in the main experiments. The complete domain order specifications tabulated in Table 9, Table 10, Table 11, Table 12.
In
Table 9. Task orders Office-Homee.
Office-Home
Task
Task 2
Task 3
Task 4
Order 1
Art
Clipart
Product
Order 2
Clipart
Art
Real_Word
Product
Order 3
Product
Clipart
Art
Real World
Order 4
RorldWorld World
Product
Clipart
Art
Order 5
Art
Real_World
Product
Clipart
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Addressing Imbalanced Domain-Incremeen Incremental Learning through Collaborative Experts
Table 10. Task orders of DomainNet.
DomainNet
Task
Task 2
Task
Task 4
Task
Task 6
Order 1
clipart
infograph painting quickdraw sketch clipart
painting sketch
quickdraw
real
sketch
Order 2
infograph
clipart
quickdraw clipart
real
Order 3
painting real
real
sketch
infograph
Order
painting
infograph
quickdraw infograph
clipart
Order 5
sketch
quickdraw
real
painting
Table 11. Task orders of CORe50.
CORe50
Task 1
Task 2
Task
Task 4
Task 5
Task
Task 7
Task 8
Order 1
s1l
s4
s2
$9
sl
s6
s5
s8
Order 2
s2
89
s1
s6
s5
s8
sl
$4
Order 3
s4
81
89
s2
s5
s6
s8
sl
Order 4
s1
89
$2
s5
s6
s8
s11
s4
Order 5
s9
82
85
s6
s8
s11
s4
sl
Table 12. Task orders of CDDB Hard.
CDDB-Hard
Task 1
Task 2
Task 3
Task 4
Task 5
Order
wild
whichfaceisreal biggan gaugan
san wild wild
gaugan whichfaceisreal
biggan
Order gaugan
san
Order
whichfaceisreal
san
biggan
Order gaugan
whichfaceisreal
wild
biggan
Order wild
biggan
gaugan
whichfaceisreal
F44 Implementation Details
Each expert is implemented three-layer MIP with layer widths set to D, D/2, and [>', respectively. The expert selector adopts similar architecture, except that the final layer outputs the number of experts instead of class logits. During VPT training, we set the number of prompts to 10. The batch size is fixed at 128, and we use cosine learning rate decay schedule with an initial learning rate 0.01. In the first training stage, we train the model for 20 epochs on the Office-Home, CORe50, and CDDB-Har datasets, and for 30 epochs on DomainNet. In the second stage, the model trained for 10 epochs on all datasets.
For each task, the number of stored parameters is 3 x D D (Dx ()). The first term corresponds to the parameters of the experts, while the second term represents the stored feature statistics (i.e., the covariance matrix and the feature mean of each class). In addition, the parameter size of our expert selector D x 2 number experts.
D x 3b, where 3b denotes the
G. Compared Methods
In this section, we introduce the methods that were compared in the main paper. Note that we re-implement all methods using the same pre-trained model as initialization. They are listed as follows.
iCaRL (Rebuffi al., 2017) is exemplar-based method, which addresses forgetting by storing representative exemplars from previous tasks (i.e., in this paper, save 10 exemplars per class) and replay them with new task data during training. It integrates knowledge distillation with exemplar retention. Beyond standard classification loss for new tasks, enforces consistency between the current and previous models' logits via distillation loss. While this dujl-objective strategy mitigates forgetting, its performance degrades under strict memory budgets due to linearly growing exemplar requirements.
18

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Addressing Imbalanced Nomain-Ineremeetn Learning through Dual-Balance Collaborative Experts
MEMO (Zhou al., 2023b) is an expansion-based continual learning algorithm that adopts dynamic architecture
expansion approach, selectively growing network components capture task-specific features while freezing existing modules. As for the implementation, we follow the original paper to decouple the network and expand the last transformer block for each new task.
SimpleCIL (Zhou et al., 2024a) proposes this simple baseline in pre-trained model-based continual learning. pretrained model by freezing backbone parameters and constructing cosine classifier from class prototypes. computes prototype vectors as feature centroids and directly assigns them as classifier weights, eliminating the need for iterative fine-tuning.
RanPAC (McDonnell al., 2023) extends SimpleCIL by randomly projecting the features into the high-dimensional space and learning the online LDA classifier for final classification.
L2P (Wang et al., 2022c) the first work introducing prompt tuning in continual learning. With the pre-trained weights frozen, learns prompt pool containing many prompts. During training and inference, instance-specific prompts are selected produce the instance-specific embeddings.
DualPrompt (Wang et al., 2022b) extends L2P in two aspects. Apart from the prompt pool and prompt selection mechanism, it further introduces prompts instilled at different depths and task-specific prompts. During training and inference, the instance-specific and task-specific prompts work together to adjust the embeddings.
CODA-Prompt (Smith et al., 2023) aims to avoid the prompt selection cost in L2P. It treats prompts in the prompt pool as bases and utilizes the attention results to combine multiple prompts as the instance-specific prompt.
S-iPrompt (Wang et al., 2022a) is specially designed for pre-trained model-based domain-incrementtl learning. learns task-specific prompts for each domain and saves domain centers in the memory with K-M During inference, it first forwards the features to select the nearest domain center via KNN search. Afterward, the selected prompt will be appended to the input.
19


Reviews:
[{"review_id": "hEoqnHZQVA", "reviewer": "Reviewer_xasy", "summary": "The paper introduces Dual-Balance Collaborative Experts (DCE), a novel framework designed to address two key challenges in domain-incremental learning (DIL) under class-imbalanced conditions:\n\n1. Intra-domain class imbalance, where some classes have significantly fewer samples than others within the same domain, leading to underfitting in few-shot classes.\n\n2. Cross-domain class distribution shifts, where the distribution of classes changes across domains, making it difficult to balance knowledge retention and adaptation.\n\nThe key techniques proposed are frequency-aware expert modules, which handle different class frequency levels (many-shot, medium-shot, and few-shot), and a dynamic expert selector, which addresses cross-domain shifts. Experiments on ViT pre-trained on ImageNet-21K and ImageNet-1K demonstrate DCE's effectiveness over the baselines.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are grateful to the reviewer for their suggestions, which enhanced our paper.\n\n**[Reviewer xasy(Claims1-1,Claims1-2), eXBQ(E3)] Effectiveness of multiple experts.**\n\nIn Section 5.3, we discussed the effectiveness of multiple experts. To address the reviewers' concerns more thoroughly, we conducted additional ablation studies, **with the results presented in Table 1 of the anonymous link [1]**.\n\nAs shown in results, when using only a single expert, the expert trained with $l_{bal}$ performs best. When employing two experts, the combination of $l_{ce} + l_{rev}$ yields the best results. The experimental results indicate that more balanced experts tend to achieve better performance. Specifically, experts trained with $l_{ce}$ and $l_{ce} + l_{bal}$ are more beneficial for many-shot classes, whereas those trained with $l_{rev}$ and $l_{rev} + l_{bal}$ perform better for few-shot classes. Our three-expert approach can be viewed as a combination of $l_{bal}$ and $l_{ce} + l_{rev}$, which achieves superior results compared to using a single expert or two experts.  \n\nTo further investigate whether three losses are sufficient, we introduced a fourth expert with $l_4=-\\log \\frac{\\exp \\left(v_{y}^{4}+3 \\log p_{b}^{y}\\right)}{\\sum_{j \\in|\\mathcal{Y}|} \\exp \\left(v_{j}^{4}+3 \\log p_{b}^{j}\\right)}$. The results indicate that the fourth expert provides minimal improvement and may even have adverse effects. Therefore, we conclude that three experts are a more suitable choice. We will incorporate this analysis into the revised version of our paper.\n\n\n**Claims1-3**: Please refer to our response to Reviewer udEf in the section \"Questions on many/medium/few-shot class division.\"\n\n**Relation 1**：Thank you for your insightful suggestion. \n- Our method is not well suited for direct comparison with ROW under a CIL setting.\n  - Motivation-wise, ROW is similar to methods we analyzed, such as S-iprompt. Row train task-specific parameters and use an OOD detector to select them during inference. This approach works well in CIL, where tasks have different label spaces, so using the corresponding task’s parameters usually leads to the best performance. However, our method is designed for imbalanced DIL. In DIL, tasks share the same label space. And due to class imbalance, few-shot classes in one task can benefit from data from other tasks. This means using task-specific parameters is may not optimal for few-shot classes. Selecting task-specific parameters for prediction mitigates many-shot class forgetting in past tasks but fails to enhance few-shot class performance using knowledge from new tasks. One of our motivation is to address this issue, which conflicts with the CIL.\n  \n  - ROW is a replay-based CIL method, whereas our approach try to solve a replay-free DIL problem. Since we do not store original samples, our method is difficult to compare with ROW under the CIL setting.\n\n- Following your suggestion, **we conducted a comparison with ROW under the imbalanced DIL setting, and the results are reported in the Table3[1].** The original ROW does not outperform our approach. **We will include ROW as a baseline method in the revised version.**\n- In future research, combining our approach with ROW could be a promising direction for addressing imbalanced DIL.  For example, modifying ROW’s OOD detection mechanism so that if past task samples can still be well classified under new parameters, they are not treated as OOD samples. However, this would require further exploration in future work.\n\n\n**Relation 2**：The contributions of our work are twofold. First, we identify two commonly overlooked challenges in DIL: intra-domain class imbalance and cross-domain class distribution shifts. These challenges naturally arise in commonly used DIL datasets such as DomainNet and OfficeHome, yet prior research has largely ignored them. We also highlight a crucial difference between two prevalent incremental learning paradigms in handling this issue (discussed in Section 3.2): shared prompt methods facilitate knowledge sharing across tasks, leading to forgetting for many-shot classes while improving few-shot class performance, whereas domain-specific prompt methods exhibit the opposite effect. Second, based on our findings, we propose a novel solution DCE with two coupled components, integrating the strengths of both paradigms to address the two challenges. Given that incremental learning and class imbalance learning are both fundamental topics in machine learning, we believe our discoveries contribute meaningfully to the ML community. Reviewer udEf also acknowledged the significance of our contributions.\n\n**Suggestions**: We appreciate your suggestions and will address them in the revised version.\n\n**Questions**: Please refer to our response to Reviewer eXBQ under the Scalability section.\n\n\n[1]https://docs.google.com/spreadsheets/d/1lTmW7KBOpFPDM7FInYMTwlwP-ULQb_13-r8Vl7EfT3M/edit?usp=sharing This link contains all tables referenced in the rebuttal.", "claims_and_evidence": "1. The DCE framework mitigates intra-domain class imbalance and improves few-shot learning performance: \n     1) It is unclear whether one of the three loss functions contributes more to the improvement or if the improvement results from their combination.\n     2) It is also unclear whether the three different loss frequencies are sufficient.\n     3) The definitions of many-shot, medium-shot, and few-shot classes are not clearly explained.", "methods_and_evaluation_criteria": "The method and evaluation criteria make sense for the problem", "theoretical_claims": "There is no theoretical justification/study in this paper except the derivation of a loss function (Eq. 3). I checked the derivation (Appendix A) and did not see any issue.", "experimental_designs_or_analyses": "The experiment designs make sense", "relation_to_broader_scientific_literature": "Contribution to CL:\n\n- The proposed technique appears to be applicable to other CL problems, such as class-incremental learning. Therefore, the paper should also compare this method against existing continual learning approaches, such as [1].\n- [1] examines the effect of out-of-distribution (OOD) detection in continual learning and demonstrates the learnability of CL. OOD detection can be useful for expert selection, potentially enhancing the learnability of DIL.\n\nLimitation:\n- Since the paper merely proposes a technique without theoretical justification, its impact is limited.\n\n[1] Learnability and Algorithm for Continual Learning. ICML 2023", "essential_references_not_discussed": "Refer to Relation to Broader Literature", "other_strengths_and_weaknesses": "NA", "other_comments_or_suggestions": "Minor typo: in line 267, \"capabilities\" appeared twice.\n\n\"Specifically, due to cross-domain label distribution shift, the experts trained in the new domain may have stronger representation **capabilities** **capabilities** for few-shot classes in the old domain.\"", "questions_for_authors": "Each task introduces 3 modules. Therefore, it's not scalable if there are many tasks. Please discuss scalability, memory consumption, and computational efficiency.", "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": "", "extra_scores": {}}, {"review_id": "Cr0hGdub15", "reviewer": "Reviewer_N4e7", "summary": "This paper addressed the problem of imbalanced domain-incremental learning, where the imbalance includes intra-domain class imbalance and cross-domain class distribution shifts. A Dual-Balance Collaborative Experts (DCE) framework is proposed, which first trains frequency-aware expert networks separately to mitigate intra-domain imbalance, and then employs a dynamic expert selector with the synthesized balanced pseudo-features to balance knowledge retention and transfer. Experiments were conducted on four benchmarks, including DomainNet, CDDB-Hard.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your suggestions to help us improve the paper.\n\n**W2**: Our paper does not claim that \"integrating new patterns for the new domains will not influence the learned patterns in previous domains.\" Instead, our goal in imbalanced DIL is to strike a balance between the two.\n\nAs detailed in Sec 3.2, existing PTM-based methods follow two main paradigms:\n  - Shared prompt: Select prompts from the prompt pool based on sample features and use them with the feature encoder for prediction. This allows old tasks to use new prompts, risking forgetting in many-shot classes but potentially improving few-shot classes.\n\n  - Domain-specific prompts: Each task has a dedicated prompt, applied based on the test sample’s domain.  While this prevents forgetting when domain prediction is accurate, few-shot classes cannot benefit from cross-domain knowledge sharing. \n\nThis limitation is illustrated in Fig 2. Our method tackles these challenges by balancing old knowledge retention and new knowledge learning. DCE trains multiple frequency-aware experts per task using different loss functions. This approach not only mitigates intra-class imbalance but also, like domain-specific prompts, preserves full expert parameters for each task, preventing forgetting caused by uncontrolled parameter combinations in shared prompt methods. \n\nTo enhance cross-task knowledge sharing, we introduce an expert selector in a two-stage training process. It assigns expert combinations to class samples within each domain, mitigating few-shot class performance degradation due to limited cross-domain knowledge sharing in domain-specific prompts. If an old domain has learned strong patterns, it prioritizes its expert; otherwise, if the new expert performs better, the expert selector adjusts the weight accordingly. The expert selector balance old and new knowledge because it is trained by sampling an equal number of features across tasks and classes. This ensures appropriate expert weight allocation across domains for effective integration. Since the feature encoder remains fixed, the sampled features stay stable during expert selector training, unaffected by domain shifts.\n\n**W1**: Intra-domain class imbalance and cross-domain class distribution shifts are two common challenges in DIL. For example, the datasets used in our experiments, Office-Home and DomainNet, naturally exhibit such distributions. However, existing works often overlook this issue and typically split test data based on the class distribution of the training set. In our setting, we construct a class-balanced test set to ensure a fair evaluation across different classes. \n\nAdditionally, as detailed in our response to W2, we explicitly discuss how our method differs from existing approaches and why it achieves better performance.\n\n**W3**: The existing works [1][2][3]  focus on the exemplar-based scenario, treating stored samples as imbalanced classes, or on the CIL. Our paper primarily addresses the exemplar-free DIL setting, which differs from these works. We will refine our claim for greater precision.\n\nRegarding Figure 2, the stronger baseline on $b_1$ demonstrate our method’s effectiveness in handling intra-domain class imbalance. To assess the impact of cross-domain class distribution shifts on different methods, it is essential to analyze the performance trends of various classes throughout the incremental training process.\n\n**Q1**: Figure 2 illustrates the performance trajectory of test samples from the first domain throughout training. The categorization into many-shot, medium-shot, or few-shot varies across domains. In Figure 4, we present the number of samples per class for each task, ensuring class IDs are consistent across subfigures. Additionally, Fig 6 analyzes class performance drift, capturing performance trends across domains for different test groups. In the revised version, we will refine this explanation and enhance Fig 4 by adding threshold markers on the vertical axis and specifying the number of categories in each group for each domain.\n\n**Q2**: The new illustration is in the second page of link [4].\n\n**Q3**: The concern regarding the correctness of Eq.(4) appears to stem from a misunderstanding. We formulate Eq.(4) as a unified loss because, during training, all three loss functions are computed on the same batch of input $x$ rather than being optimized independently.\n\n**Q4**: Please refer to our response to Reviewer udEf in the section \"Questions on many/medium/few-shot class division.\"\n\n**We will adjust the paper according to your suggestions to make it clearer and more readable.**\n\n[1] Rethinking Class-Incremental Learning from a Dynamic Imbalanced Learning Perspective\n\n[2] Long-Tailed Class Incremental Learning\n\n[3] Long-Tail Class Incremental Learning via Independent SUb-Prototype Construction\n\n[4] https://docs.google.com/spreadsheets/d/1lTmW7KBOpFPDM7FInYMTwlwP-ULQb_13-r8Vl7EfT3M/edit?usp=sharing", "claims_and_evidence": "partially, see below of weaknesses", "methods_and_evaluation_criteria": "yes", "theoretical_claims": "yes", "experimental_designs_or_analyses": "yes", "relation_to_broader_scientific_literature": "The paper could have broader scientific impact on class-imbalanced learning, domain-incremental learning, multimodal continual learning, and mixture of experts.", "essential_references_not_discussed": "no", "other_strengths_and_weaknesses": "Strengths:\n1. The paper is well organized and easy to read.\n2. The proposed method is evaluated on four benchmarks, and demonstrates better results than the compared methods.\n\nWeaknesses:\n1. The two issues addressed in this paper are two common ones for domain-incremental learning, however, it is unclear about the innovations of the proposed method in dealing with them compared to existing works. It also lacks in-depth analysis of why the proposed solution could achieve better results when dealing with these problems.\n2. The paper did not describe clearly why it can preserve knowledge of many-shot classes while integrating few-shot patterns for new domains, why integrating new patterns for the new domains will not influence the learned patterns in the previous domains.\n3. While existing works may not explicitly claim about dealing with class imbalance, this issue is implicitly considered. Claiming this work as the first one to explore this issue is exaggerated. In addition, there is no solid ablation to support this argument. The good property of DCE in Figure2 may be due to the better baseline in a single-domain setting, i.e., better results in b1 (no domain-incremental is involved) than its competitors.\n\n\n==================== post rebuttal =======================\n\nAfter read the rebuttal and other reviews, the reviewer maintains the initial recommendation.", "other_comments_or_suggestions": "none", "questions_for_authors": "About Fig2, does the specific classes falling into {many-shot, medium-shot, few-shot} differs across the domains or not? If not, then it did not reflect the claimed class imbalance issue encountered in the DIL setting. If it is, it needs to clarify and is better to provide an analysis about how the dynamics of these classes influence the final results. For instance, the ratio of few-shot classes in the whole dataset, or the specific classes in a given ratio of few-shot classes.\n\nIt would be better if the authors can provide some illustrations to help understanding how the proposed method works in dealing with mentioned two issues.\n\nThe Equ.(4) is not correct, as the three experts were trained independently using one of three terms in Equ.(4). In other words, the network was never trained using Equ.(4). \n\nThe appendix described how to construct the imbalanced training sets for CORe50 and CDDB-Hard, it is still unclear how to divide them into many/medium/few-shot classes.", "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": "", "extra_scores": {}}, {"review_id": "FynG0B0VXp", "reviewer": "Reviewer_eXBQ", "summary": "This paper introduces a practical task, Imbalanced Domain-Incremental Learning, which involves both intra-domain class imbalance and cross-domain class distribution shifts. To address this task, the authors propose the Dual-Balance Collaborative Experts (DCE) framework, which leverages a multi-expert collaborative approach. The method demonstrates superior performance in a series of extensive experiments.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your suggestions.\n\n**[Reviewer xasy(Q), N4e7(E1)] Scalability**  \n\nWe analyze scalability from two aspects: memory consumption and computational efficiency.\n- Memory Consumption:  \n  In incremental learning, it is common to retain certain past task information to mitigate forgetting. Among the compared methods, L2P maintains a prompt pool and corresponding keys per task. Our DCE  retains three expert networks, class mean/covariance, and a shared expert selection network.  \n  To provide a quantitative comparison, we report the number of parameters retained outside the feature encoder for different methods at the end of the last task on the Office-Home dataset, as shown in Table 2 of the anonymous link [1].\n\n- Computational Efficiency:  \n  As shown in Table 2, DCE requires more parameters to be learned and stored compared to some baseline methods, primarily due to the presence of multiple experts in our model. However, our approach remains computationally efficient due to the following reasons:  \n  - In contrast to baseline methods such as L2P, DualPrompt, which require **two forward passes through the feature encoder** during both training and inference, DCE requires only **a single forward pass**. This is because baseline methods first obtain an embedding from the original feature encoder, use this embedding to select prompts, and then recompute a second forward pass with the selected prompts. In contrast, DCE directly processes the input in a single pass, significantly reducing training and inference costs.  \n  - After the first task, only expert parameters are updated, and gradients do not propagate through the feature encoder, avoiding the need to construct a computational graph over it. Thus DCE reduces the computational burden during training compared to other methods.  \n  - To further validate our efficiency, we conducted experiments on an RTX 3090 GPU and recorded the average time per batch during training and inference for different methods under the same batch size. The results are reported in Table 2 of the anonymous link [1].  \n\nIn the revised version, we will supplement our analysis of scalability, memory consumption, and computational efficiency to provide a more comprehensive discussion.\n\n\n**E2**:  Since our work is the first to explore the PTM-based imbalanced DIL problem, we primarily compare against commonly used PTM-based CIL/DIL approaches. To ensure fairness, we also modified the baseline methods by replacing their cross-entropy loss with balanced cross-entropy loss, a widely adopted approach for class imbalance. The results, shown in the third column of Figure 5, demonstrate that our method still outperforms the baselines. Additionally, we compared our approach with DUCT [2], a recently published DIL method in CVPR 2025, and reported the results in Table 3 of the anonymous link [1]. Our method also achieves superior performance over this latest approach.\n\n**E3**：Besides accuracy, we also report other evaluation metrics. For instance, inspired by the forgetting measure in CIL, we propose a new metric, CPD (shown in Figure 6), to track the performance variation of each class throughout training. For further analysis and discussion on different experts, please refer to our response to Reviewer xasy in the \"Effectiveness of multiple experts section\".\n\n**Weakness and Questions**：\nRegarding novelty, we address an overlooked yet prevalent issue in DIL: intra-domain class imbalance and cross-domain class distribution shift, naturally present in Office-Home and DomainNet. We analyze why existing incremental learning paradigms struggle with these challenges—shared prompt paradigms suffer from catastrophic forgetting in many-shot classes while benefiting few-shot ones, whereas Domain-specific prompts mitigate forgetting but fail to help few-shot classes. Our key insight, previously unexplored, drives our approach: balancing knowledge retention and new task learning in imbalanced DIL. If you have further concerns, please refer to our response to Reviewer N4e7 [W2, Q2]. Instead of simply combining \"better solutions,\" our method integrates the strengths of both paradigms. Reviewer xasy considers our work a \"novel framework,\" and Reviewer udEf also acknowledges our contributions.\n\nAs you pointed out, intra-domain class imbalance and cross-domain class distribution shift are not independent but rather different perspectives on the same phenomenon. Correspondingly, the two components of our DCE framework—frequency-aware experts and dynamic expert selector—are not separate but are designed to work together to tackle these challenges. This further reinforces that our method is not merely a combination of \"better solutions.\"\n\n[1]https://docs.google.com/spreadsheets/d/1lTmW7KBOpFPDM7FInYMTwlwP-ULQb_13-r8Vl7EfT3M/edit?usp=sharing This link contains all tables referenced in the rebuttal.\n\n[2] Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning. CVPR2025.", "claims_and_evidence": "Yes, the method proposed in this paper solves the introduced task.", "methods_and_evaluation_criteria": "Yes, the proposed method has real-world application significance.", "theoretical_claims": "There is no much mathematical theory in the paper.", "experimental_designs_or_analyses": "The authors have conducted comprehensive experiments, effectively demonstrating the superiority of the proposed method compared to existing CIL/DIL approaches. However, I believe there are still some shortcomings in the experiments:\n\n1. The proposed method appears to be more complex than existing baselines, and while the authors claim that it \"significantly reduces computational overhead,\" a comparison with existing methods in terms of time and space costs would be helpful for clarification.\n\n2. While the authors address the issue of class imbalance, the baselines used for comparison are all conventional CIL/DIL methods, which might not provide a fair comparison. This does not fully highlight the ability of the proposed method to handle class imbalance. It would be beneficial to include a wider range of baselines or more robust methods in the comparison.\n\n3. The experiments focus solely on accuracy (Acc) comparisons and exploration. It would be valuable to conduct more in-depth experiments using a broader set of metrics to gain additional insights into the method. For example, exploring the practicality of the method with three different experts could provide more comprehensive results.", "relation_to_broader_scientific_literature": "The authors have extended the existing DIL task based on the setting of class imbalance, which demonstrates broad relevance to the existing literature.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "1. The paper is well-motivated and seems to be reproducible.\n2. The process of the method is clear and I think it is reproducible.\n\nHowever, I believe the authors need to further clarify the innovation of their method. The proposed approach lacks a clear sense of novelty and seems more like a combination of \"better solutions,\" especially with the introduction of the Frequency-Aware Experts. A targeted comparison and explanation, along with task-specific reasoning, might help highlight the true innovation of the method.", "other_comments_or_suggestions": "None", "questions_for_authors": "The authors' summary of the two challenges in the task seems somewhat overlapping. In my understanding, intra-domain class imbalance and cross-domain class distribution shifts can be viewed as aspects of the broader issue of class imbalance. The second challenge appears to address catastrophic forgetting. This is just my personal interpretation, and I have some considerations regarding the method's innovation and the completeness of the experiments. While I currently hold a positive evaluation, my opinion might be swayed by the assessments of other experts in continual learning and imbalanced learning.", "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": "", "extra_scores": {}}, {"review_id": "B7c6GfDVev", "reviewer": "Reviewer_udEf", "summary": "The paper introduces the Dual-balance Collaborative Experts (DCE) framework to address imbalanced domain-incremental learning. The key challenges tackled are intra-domain class imbalance and cross-domain class distribution shifts. DCE employs two main components: (1) ​frequency-aware experts trained with specialized loss functions to decouple feature learning for many-shot, medium-shot, and few-shot classes, and (2) a ​dynamic expert selector that synthesizes pseudo-features via Gaussian sampling from historical class statistics to balance knowledge retention and transfer. Experiments on four benchmarks demonstrate SOTA performance, particularly in improving few-shot class accuracy while mitigating catastrophic forgetting of many-shot classes.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**[Reviewer xasy (Claims1-3), N4e7 (Q4), udEf (W1, Q2)]: Questions on many/medium/few-shot class division**\n\nThank you for your questions. Reviewer xasy and N4e7 raised similar concerns, which might be due to our explanation of dataset division being placed in Appendix E.2. The division of many/medium/few-shot classes is solely for the convenience of description and experimental results presentation in the paper and is not directly related to our proposed algorithm. The loss functions of the three frequency-aware experts rely only on the class frequencies in the training data and do not depend on the class division. In our experiments, following the settings in [1], we used (20, 60) and (20, 100) as thresholds to divide the many/medium/few-shot classes within each domain of the Office-Home and DomainNet datasets. And we sample class-balanced test sets from the corresponding domains. Unlike these two datasets, CORe50's test set is not sampled from each task’s data but consists of three outdoor sessions, making it impossible to directly align test set classes with the corresponding domain class divisions. Additionally, each task in CDDB-Hard is a binary classification problem. As a result, these two datasets do not undergo many/medium/few-shot class division. We will provide a more detailed explanation in the revised version to prevent misunderstandings.\n\n**Q1**: The three frequency-aware experts in DCE are trained with different loss functions, each favoring classes of different frequencies. The distinct optimization objectives lead to diversity in predictions among the three experts. For further analysis, please refer to our response to Reviewer xasy under \"Effectiveness of multiple experts.\"\n\n**Q3**:  As mentioned above, CORe50's test set is derived from three outdoor sessions. Thus, forgetting has a relatively minor impact on this dataset, whereas knowledge sharing across tasks is more crucial. Most methods exhibit performance improvements as training progresses. However, prompt-specific methods, such as S-iPrompt, lack knowledge sharing across tasks, resulting in inferior performance compared to other approaches. The results on CORe50 further demonstrate that our method enables better knowledge sharing compared to prompt-specific methods like S-iPrompt.\n\n**Q4, W3**:  Class Performance Drift (CPD) is a new metric we introduced, inspired by the forgetting measure in CIL. CPD quantifies the performance change of test data from domain $b$ after training on domain $b$ is completed compared to the final model trained on all domains. It is computed as $\\text{CPD} = \\frac{1}{B-1}\\sum_{b=1}^{B-1}(\\mathcal{A}_b-\\mathcal{A}_B)$. SimpleCIL exhibits a lower CPD because it lacks a training process and only constructs class prototypes for classification in each task. It sacrifices model performance in exchange for lower CPD.\n\n**Q5**：Our motivation can be further explained through the CPD results. As shown in Figure 6, shared prompt-based methods exhibit more significant performance degradation in many-shot and medium-shot classes but greater performance improvements in few-shot classes. In contrast, domain-specific prompt-based methods show the opposite trend. Our DCE method strikes a balance between these two extremes, aligning well with our intended motivation.\n\n**W2**: For additional clarification on Figure 2, please refer to our response to Reviewer N4e7 (W3, Q1). On one hand, we apply VPT to adapt the model to each task. On the other hand, we need to sample in the feature space to train the expert selector. Therefore, we choose the MLP trained after the feature encoder as the expert for each task and fix the feature encoder in subsequent tasks to maintain the stability of the feature space used for sampling. Some comparison methods, such as RanPAC, also adopt a similar approach, where VPT fine-tuning is only conducted for the first task.\n\n**W4**: We assure that the complete code will be released after the paper is accepted.\n\n[1] On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond.", "claims_and_evidence": "Yes. The primary motivation of this paper lies in its discovery that in ​DIL, ​sharing knowledge leads to improved performance for minority classes but causes forgetting in majority classes. Conversely, ​not sharing knowledge reduces forgetting in majority classes but fails to enhance the performance of minority classes. This motivation is clear, intuitively correct, and empirically validated through the experimental results presented in ​Figure 2.", "methods_and_evaluation_criteria": "Yes. The dual-phase training is logically sound. Frequency-aware losses and Gaussian-sampled pseudo-features are appropriate for addressing class imbalance and distribution shifts. Benchmarks are well-chosen, and metrics align with the problem’s requirements.", "theoretical_claims": "Yes. Equation (3) derivation is correct under the assumption that the target distribution $p^​(y)$ is inversely proportional to the source distribution $p(y)$.\nOAS covariance regularization is a valid approach for stabilizing imbalanced class statistics.", "experimental_designs_or_analyses": "The paper conducts comprehensive experiments on common benchmark datasets and compares the proposed method with SOTA DIL approaches, including ​shared prompt and ​domain-specific prompt methods, as well as ​exemplar-based methods. The experiments are evaluated using multiple metrics, and the results demonstrate superior performance, highlighting the robustness of the proposed method.", "relation_to_broader_scientific_literature": "Compared to existing methods, this paper places greater emphasis on addressing the imbalance issues inherent in DIL. It provides a detailed analysis of the limitations of prior DIL approaches in the context of imbalanced DIL scenarios and offers a targeted solution to these challenges.", "essential_references_not_discussed": "Currently, no papers related to this field that have not been cited have been identified.", "other_strengths_and_weaknesses": "Strengths:\n1. One significant contribution of this work lies in proposing a new ​imbalanced DIL setting, where class distributions within each domain are both imbalanced and distinct across domains. This is a highly practical issue that has been largely overlooked in prior research. \n2. The paper is ​well-motivated and its greatest strength is the empirical dissection of existing methods’ failures in imbalanced DIL. Specifically, it reveals the inherent ​trade-off between leveraging shared knowledge to improve few-shot class performance and preserving historical knowledge to mitigate catastrophic forgetting of many-shot classes. This finding is highly insightful and contributes significantly to the field.\n3. The proposed DCE aligns seamlessly with the authors' motivation. The ​frequency-aware experts and ​dynamic expert selector are ​organically integrated to address the two key challenges in imbalanced DIL. The method proposed in this paper is ​not complex, yet it is ​highly intuitive, ​innovative, and ​feasible in practice. \n4. The experiments in this paper are ​thorough and comprehensive. The authors compare the proposed method with multiple SOTA approaches across ​several benchmarks and provide a detailed analysis of the results. These experiments effectively demonstrate the ​superior performance and ​effectiveness of the proposed method.\n5. The structure of the paper is clear, making it easy for readers to follow and understand.\n\nWeaknesses：\n1. Some of the experimental settings in the paper require ​further clarification. The criteria for partitioning classes into many-shot, medium-shot, and few-shot groups are not explicitly defined.  And the design of the expert networks need further elaboration.\n2. Figure 2 requires further explanation to improve its clarity and ease of understanding. Specifically, the rationale for training the prompt parameters $\\theta_1$ ​only during the first task via VPT is not explicitly discussed. It is recommended to provide a ​more detailed explanation of the figure in the ​caption of Figure 2.\n3. The paper introduces ​Class Performance Drift (CPD), inspired by the ​Forgetting Measure, to evaluate the change in class performance during training. However, the description of how CPD is calculated appears somewhat unclear. Providing a ​mathematical formula to define CPD would significantly enhance the readability and clarity of the paper.\n4. The ​source code is not included in the supplementary material. Publicly releasing the code would enhance reproducibility and adoption.", "other_comments_or_suggestions": "Refer to weakness and questions.", "questions_for_authors": "1. How does DCE ensure ​diversity among experts trained for each domain? \n2. The paper explicitly defines many-shot, medium-shot, and few-shot classes for Office-Home and DomainNet but does not apply this categorization to CORe50 and CDDB-Hard. Why is this distinction omitted for these datasets? \n3. In ​Figure 5, why does the accuracy on ​DomainNet decrease as the training progresses, while the accuracy on the ​CORe50 dataset generally increases?\n4. In ​Figure 6, according to the ​Class Performance Drift metric, the CPD of ​SimpleCIL appears to be lower than that of the proposed ​DCE method, yet its overall performance is inferior to other methods. How can this phenomenon be explained?\n5. In the experimental section, are there additional results or analyses that could ​further support the paper’s motivation?", "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": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper introduces Dual-Balance Collaborative Experts (DCE), a novel framework designed to address the challenges of domain-incremental learning (DIL) under class-imbalanced conditions. These challenges include intra-domain class imbalance, where some classes have significantly fewer samples within the same domain, and cross-domain class distribution shifts, where the class distribution changes across domains. DCE employs frequency-aware expert modules to handle different class frequency levels (many-shot, medium-shot, and few-shot) and a dynamic expert selector to balance knowledge retention and adaptation across domains. The method is evaluated on ViT pre-trained on ImageNet-21K and ImageNet-1K, demonstrating its effectiveness over baselines.

Some of the main strengths of the paper that are highlighted by the reviewers are:
1. The paper tackles two key challenges in domain-incremental learning (DIL) under class-imbalanced conditions: intra-domain class imbalance and cross-domain class distribution shifts.
2. The paper introduces frequency-aware expert modules, which handle different class frequency levels (many-shot, medium-shot, and few-shot).
3. The paper introduces a dynamic expert selector, which addresses cross-domain shifts.
4. One of the main strengths of this paper is the empirical results on existing methods’ failures in imbalanced DIL. Specifically, it reveals the inherent trade-off between leveraging shared knowledge to improve few-shot class performance and preserving historical knowledge to mitigate catastrophic forgetting of many-shot classes.

The reviewers highlighted several weaknesses:
1. There was a lack of clarity when it came to the different class divisions, namely, what constituted many, medium, and few shot.  This was addressed in the author rebuttal.
2. There was insufficient discussion on scalability given that there are multiple expert modules.  This was addressed sufficiently in the author rebuttals.
3. The reviewers pointed out that there were insufficient baselines, and the authors included two new baselines in the rebuttal.
4. Given that it seems that the main issues that were raised by the reviewers were addressed in the rebuttal, I think this paper merits acceptance.
Decision: Accept (poster)