Recall and Refine: A Simple but Effective Source-free Open- set Domain Adaptation Framework
Authors: Ismail Nejjar
Hao Dong
Olga Fink
Venue: Accepted by TMLR ()

Abstract: Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes — also referred to as target-private unknown classes — are present. Source-free Open-set Domain Adaptation (SF-OSDA) methods address OSDA without accessing labeled source data, making them particularly relevant under privacy constraints. However, SF-OSDA presents significant challenges due to distribution shifts and the introduction of novel classes. Existing SF-OSDA methods typically rely on thresholding the prediction entropy of a sample to identify it as either a known or unknown class, but fail to explicitly learn discriminative features for the target-private unknown classes. We propose Recall and Refine (RRDA), a novel SF-OSDA framework designed to address these limitations by explicitly learning features for target-private unknown classes. RRDA employs a two-stage process. First, we enhance the model’s capacity to recognize unknown classes by training a target classifier with an additional decision boundary, guided by synthetic samples generated from target domain features. This enables the classifier to effectively separate known and unknown classes. Second, we adapt the entire model to the target domain, addressing both domain shifts and distinguishability to unknown classes. Any off-the-shelf source-free domain adaptation method (e.g.\ SHOT, AaD) can be seamlessly integrated into our framework at this stage. Extensive experiments on three benchmark datasets demonstrate that RRDA significantly outperforms existing SF-OSDA and OSDA methods.

Published in Transactions on Machine Learning Research (05/2025)
Recall and Refine: A Simple but Effective Source-free Open- set Domain Adaptation Framework
Ismail Nejjar EPFL
ismailneejar@eppf.cc
Hao Dong ETH Zürich
hao. dongghbk..bbugg etha. ch
Olga Fink EPFL
fink
Reviewed on OpenReview: https: // openreview. net/ forum? id= HBZoXjUAqq
Abstract
Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes also referred to as target-private un- known classes are present. Source-free Open-set Domain Adaptation (SF-OSDA) meth- ods address OSDA without accessing labeled source data, making them particularly relevant under privacy constraints. However, SF-OSDA presents significant challenges due to distri- bution shifts and the introduction of novel classes. Existing SF-OSDA methods typically rely on thresholding the prediction entropy of a sample to identify it as cither a known or unknown class, but fail to explicitly learn discriminative features for the target-private unknown classes. We propose Recall and Refine (RRDA), a novel SF-OSDA framework de- signed address these limitations by explicitly learning features for target-private unknown classes. RRDA employs two-stage process. First, enhance the model's capacity to rec- ognize unknown classes by training target classifier with an additional decision boundary, guided by synthetic samples generated from target domain features. This enables the clas- sifier to effectively separate known and unknown classes. Second, we adapl the entire model to the target domain, addressing both domain shifts and distinguishability to unknown classes. Any off-tho-shelf sourco-free domain adaptation method (c.g. SHOT, AaD) can be seamlessly integrated into our framework al this stage. Extensive experiments on three benchmark datasets demonstrate that RRDA significantly outperforms existing SF-OSDA and OSDA methods. The code is available at |ttp://ithaccccoo////aaeeaaaaaaa
1 Introduction
Unsupervised Domain Adaptation (UDA)
et al. 2010 Ganin & Lempitsky 2015 Long et al.
2015) adapts a model from labeled source domain to an unlabeled target domain (Oza et 2023). effec-
tively addressing the issue of domain shift where the source and target distributions differ. UDA strategics
typically align feature distributions between domains using metric learning techniques Long al.
2015
Kang et al. 2019) or adversarial training |Ganin & Lempitsky 2015 Tzeng et al. 2017 Luo al.
2019
and more recently, self-training approaches (Sun et al. 2022 Hoyer et al. 2023 Zhu et al. 2023).
Despite
their success, most current domain adaptation approaches operate under the assumption of a shared label set between the source and target domains (i.e., C, = Ct), referred to as Closed-set Domain Adaptation (Seenko)
et al. 2010 However, this assumption is often impractical in real-world scenarios.
In contrast, Open-set Domain Adaptation (OSDA) extends the target label space beyond that of the source
domain (i.e., C. G.) (Saito et. al. 2018 Liu et. al. 2019), thereby adding complexity to the DA task.

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Published in Transactions on Machine Learning Research (05/2025)
OSDA aims to align target samples from known classes with those from the source domain while effectively identifying larget samples belonging to categories not observed in the source domain, referred to as unknown
classes (Panareda Busto & Gall 2017: Bucci et al., 2020 Jang et al. 2022). Various criteria based on
instance-level predictions have been proposed, including entropy-based (Feng et al. 2021 Saito et al. 2020)
and confidence-based (Saito & Saenko, 2021 Fu et al. 2020) methods.
Additionally, privacy and legal considerations increasingly limit access to labeled source data for adaptation purposes. To address this, source-free adaptation methods [Fang ct al.] 2024 have emerged, enabling
adaptation without reliance on labeled source data (Kim et al. 2021 Kundu et al. 2020a Li et al. 2020 In this paper, we focus on Source-free Open-sct Domain Adaptation (SF-OSDA), where only pre-trained source model is available for knowledge transfer, without access to labeled source data. Although some Source- free Domain Adaptation (SF-DA) methods have demonstrated effectiveness in addressing SF-OSDA for classification tasks (Liang et al., 2020 Yang et al. 2022 Wan et al. 2024). semantic segmentation (Choe et al., 2024), and graph applications (Wang et al.) 2024b), they primarily focus on the semantics of known classes in the source domain, often overlooking the crucial aspect of novel-class semantics. These methods focus on segregating target samples with low entropy, categorizing them as known classes, and subsequently optimizing specific objectives such as entropy minimization or clustering. In this process, data points associated with known classes are prioritized, while those with high entropy are typically excluded from training, leading to a semantic disparity between the known and unknown classes.
To address these limitations, we propose Recall and Refine (RRDA), a novel SF-OSDA framework that explicitly learns representations for target-private unknown classes by generating synthetic target-domain features. Our approach leverages the key insight that source classifier trained only on known classes yields high-entropy (highly uncertain) predictions for unknown samples and low-entropy (highly confident) predictions for known ones, We optimize synthetic features to exhibit these entropy characteristics, cluster the resulting high-entropy features into K' pseudo-unknown classes, and refine the source classifier's decision boundarics accordingly to accommodate novel categorics. This small change recasts open-set adaptation as a closed-set problem, allowing any olf-the-shelf source-free domain adaptation method (e.g., SHOT (Liang et al. 2020), AaD (Yang et al., 2022)) to be seamlessly integrated for joint domain-shift and unknown-class separation. Extensive experiments three SF-OSDA benchmarks demonstrate that RRDA significantly outperforms existing methods.
2 Related Work
Unsupervised Domain Adaptation (UDA) aims to adapt a model originally trained on a labeled source domain to perform effectively in an unlabeled target domain. This adaptation process assumes access to data from both the source and target domains during training (Oza ct al. 2023). UDA strategics often align Teature distributions between domains using metric learning techniques (Long el al. 2015 Kang et al.] 2019:
Nejjar et al. 2023) or adversarial training across various spaces, including image input space (Murez et al. Pizzati et al. 2020), feature space (Ganin & Lempitsky 2015), and output space (Luo et al. 2019 Vu
2018
2019). Additionally, various techniques incorporate pseudo- labulog or self-training algorithms (Sun 2022 Dong et al. 2023 Yue et al. 2024), which generate pseudo-labels for unlabeled samples in the target domain. However, existing approaches assume that label spaces are identical across both domains, limiting their applicability in real-world scenarios.
et al.
et al.
Open-set Domain Adaptation (OSDA) addressos scenarios where the target domain may contain classes not present in the source domain (Panareda Busto & Gall 2017 Dong al. 2024a b Li et al., 2021
Various approaches have been proposed to tackle this challenge, including assigning target domain images
to source categories while discarding unrelated target domain images (Panareda Busto & Gall 2017 and
using adversarial training to soparate unknown target samples (Saito et al. 2018 Jang et al. 2022 The
Separate to Adapt (STA) approach [Liu et al.) 2019) progressively separates unknown and known class samples using a coarse-to- weighting mechanism and proposes evaluating OSDA on diverse levels of openness. Rotation-based Open Set (ROS) (Bucci et al., 2020) explores the use of self-supervised tasks such as rotation recognition for unknown class detection. (Jing et al.) (2021) projects features to a hyperspherical latent space to reject known samples based on angular distance. Adjustment and Alignment for Unbiased
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Published in Transactions on Machine Learning Research (05/2025)
Open Set Domain Adaptation (ANNA) Li ct al. 2023 addresses semantic-level bias in OSDA by designing Front-Door Adjustment and Decoupled Causal Alignment modules. However, these approaches all assume the availability of labeled source data, which can pose challenges due to privacy concerns in real applications.
Source-free Domain Adaptation (SFDA) leverages only a source-trained model and unlabeled target data for adaptation to the target domain. SFDA approaches can be categorized into data-based and model- based methods (Yu et al. 2023). One of the data-driven methods, SHOT, was introduced by Liang et al.) (2020). It adapts pre-trained source model via information maximization with self-supervised pscudo- labeling to implicitly align target domain representations to the source hypothesis. Building on this approach, subsequent works(Chu ct al. 2022: Lec et al. 2022 Qu et al. 2022) refine the adaptation through self-training techniques. Other works explore different training procedures. For example, historical Contrastive Learning (HCL) (Huang et al., 2021 compensates for the absence of source data by leveraging historical models and contrasting current and historical embeddings of target samples. Some methods (Yang et al.j 2021 2022) enforce consistency between local neighbors by considering local feature density, with Attract and Disperse (AaD) Yang al.
2022) treating SFDA as an unsupervised clustering problem. Additionally, Zhhan et al.) (2023) explores leveraging source model classifier weights as class prototypes to embed class relationships
into a similarity mcasure for target sample.
Source-free Open-set Domain Adaptation (SF-OSDA) extends SFDA to scenarios where the target domain contains novel classes not present in the source domain. While methods like SHOT (Lingg et al. 2020), A&D (Yang et al.] 2022), and Uncertainty-guided Source-free Domain Adaptation (C-SFAN) (Roy et al., 2022 have been adapted for SF-OSDA, they primarily focus on the known class semantics in the source domain, which can lead to suboptimal handling of target-private unknown classes. Universal Domain Adaptation (UniDA) aims to handle domain shifts and label set differences between source and target domains, encompassing open, partial, and open-partial set scenarios (Liang et al.) 2021 Qu et al. 2023 2024). Recent SF-UniDA methods proposed one-vs-all clustering approaches (Qu et al. 2023) and subspace dccomposition (Qu et al., 2024) to separate and identify common and private target classes in a source-free setup. Similarly, Progressive Graph Learning (Luo et al. 2023) decomposes the larget hypothesis space inlo shared and unknown subspaces for SF-OSDA. However, current methods either require specific training for the source model to incorporate the unknown classes (Kundu et al. 2020b cb, which is usually impractical, rely on thresholding metric to distinguish known classes from unknown ones during training and inference, making the prediction sensitive to different thresholds.
3 Methodology
3.1
Preliminary
For SF-OSDA, we are given a source pre-trained model f& and an unlabeled target domain with n, samples, denoted as D. {x where x E X RRd The target domain follows a distinct data distribution (P' + P*) from the source domain, reflecting both distribution and label shifts.
Let C and C C y represent. the label sets for the source and target domains, respectively, where C* C C.. Both domains share K common classes referred to as known classes (Ch = C*). Additionally, target-private classes are grouped as an unknown class (Cu.. - C*). The primary objective of SF-OSDA is to classify both unknown and knoum classes, relying exclusively on the target domain data and a pre- trained source model. The pro-trained model can be decomposed as fā = = hÿ, where hi : Rd + R.D is a feature extractor and gā RD + RK is the source classifier. Unlike previous works, which freeze the source classifier (e.g. SHOT) during adaptation, we propose training new target classifier d to explicitly account for target-private unknown classes.
One of the challenges in open-set scenarios is the ability to distinguish known from unknown classes in the target domain. Different approaches have been proposed for distinguishing between known and unknown classes, including hand-crafted thresholding critcria and clustering strategics. However, paradigms such as vendor-to-client (undu et al. (220cc) are more effective, as they incorporate an auxiliary out-of-distribun classifier during source training, enabling better handling of unknown classes in the target domain.
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Published in Transactions on Machine Learning Research (05/2025)
Projected target feature the source classifier
(1) Optimization of synthetic points to show lowlhigh entropy
Pre-trained source
Feature Encoder
Classitier
bub ba
Source Domain
Weight initialiiaatinn
(2) Train new target classifier with synthelic points
Uniaballd target sample
(known and unknown classes)
of
Labeled Syrthatic data points
Classifier Weight initialization
Known daass decision boundary
Terget Domein
Uninoww class decision boundary.
Source Free adaptation
Figure 1: Overview of RRDA for Source-free Open-set Domain Adaptation. Panels (a)- respectively illustrate: (a) target-fcature extraction with the frozen source encoder, (b) ontropy-guidee "recall" of syn- thetic known/ known/unknon /unknown features, (c) "refine" training of a target classifier on these synthetic points, and final adaptation with any closod-set SF-DA objective.
In this paper, we propose a novel approach address this limitation by adapting the source classifier post hoe to include new decision boundaries for unknown classes. Our method enables the seamless adaptation of any off-the-shelf source pre-trained model to target domain, even in the presence of novel classes. Motivated by the idea that learning from unknown class samples can improve performance in open-set scenarios, our objective is to simplify adaptation and eliminate the dependency on threshold basedd methods during inference.
3.2 RRDA
Our proposed Recall and Refine framework for SF-OSDA consists of three main steps:
1. Generate synthetic features: Referring to step (b) in Figure 1 synthotic feature points are gen- erated for both known and wnknown classes. This involves optimizing larget feature representations using entropy objectives.
2. Train a target classifier: Referring to step (c) in Figure I the synthetic feature points are used to train a new target classifier df with extended decision boundaries to accommodate unknown classes.
3. Adapt the whole model: The entire model is adapted using any off-the-shelf source-free domain adaptation methods (e.g. SHOT, AaD) on target domain data.
This allows the model to (1) learn the semantics of both known and unknown classes in the target domain, (2) treat OSDA as a simple closed-set scenario, and (3) directly output predictions for unknown classes.
3.2.1 Synthetic Data Generation.
The first step of our proposed approach involves generating synthetic features for both known and unknown classes using the source classifier gÿ- Specifically, we optimize the target feature representation z' = hī((') to generate synthetic samples that exhibit low entropy for known classes and high entropy for the unknoun class. We denote these optimized synthetic features zinown and ztt The resulting unknown features are clustered into K' groups, and new target classifier d is introduced with K + K' classes.
In this sention, we describe the process for obtaining feature representations for both known and wnknown classes. We use standard gradient descent optimization to generate the desired feature representations.
Synthetic Unknown Classes Generation: To effectively identify points near the source classifier's de- cision boundary, we seek synthetic features xtt that maximize the entropy while ensuring diverse feature
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Peaturr
Fresture
Synthetic point Chass D.;
Synthetic Class
rget sharso castés rggt shared casses 1a rget-shared classes?
Synthetic point Class
Target shared
Outlier Glass
Target shared classes
Gutliey Class 1
Target-shared classes 2
rget private daasses
Outleer Class
Target private classes
Feature
Feature
(a) Unlabeled target domain pro- jected onto the decision boundary of the source classifier.
(b) Optimized synthetic points for known and unknown classes.
(c) Unlabeled target domain pro- jected onto the new decision
boundary of the target classifier.
Figure 2: Visualization of the synthetic data generation process and the resulting target classifier boundary on a toy example with K = K" = 3 classes.
representations, thereby reducing the risk of collapsing to a single point in feature space. To prevent feature collapse, we add a hinge-style variance regularizer max 1 /Var(z') computed over the entire set of synthotic features, explicitly enforcing Var(z') 2 and encouraging global diversity, similar to Bardes et al. (2022). Specifically, we initialize the optimization with a noisy version of the original features z by adding Gaussian noise drawn from (00, 1.0) to the features extracted by the source-pretrained encoder hg before any optimization.
min -H(o(gb(2')) + A max(0,1 - / Var(zz)),
(1)
where (p) = - Ei Pk log(ps) represents the entropy, and ar is the softmax activation function, and À was set to for all the experiments. After optimization, we select feature points with high entropy as unknown class candidates. Specifically, only points satisfying H (( The threshold value 0.75 analyzed in the Ablation section. The selected high-entropy features z are then clustered into K' distinct unknown classes using K-means clustering. Each resulting cluster is assigned unique label in the range Yunk E {K + 1,..., K + K'), effectively extending the label space beyond the original K known classes. This approach is motivated by the observation in the literature (Lampert et al. 2009) that it is possible to generate meaningful semantics for novel classes using known classes.
0.75 Iog(K) are considered as zunk
Synthetic Known Classes Generation: A similar optimization approach employed generate syn- thetic data points for the known classes. The optimization performed iteratively K times, once for each known class & (where & E {1....,)) The objective is to minimize the cross-entropy for each class directly from z.. The optimization problem for generating a sample for class k is defined as:
min LCel98(z'), + X max(0.1 / /Vrr(z)))
(2)
where LCE the cross-entropy loss that encourages the model to predict class k and À controls the regular- ization term, set to in all experiments. After optimization, we select feature points with low cross-entropy loss known class candidates. Specifically, only points satisfying Le (z*),, IR) < 0.25 log(K) are con- sidered as "kowwn This threshold ensures the synthetic features have high confidence for their assigned class. The specific value 0.25 is analyzed in the Ablation section.
Each synthetic feature at Z assigned the label yi = k, forming the pairs (z V6 ). These synthetic data points and their corresponding pseudo-labels are then used to train the target classifier. By iteratively generating feature points for each known class, our method enhances the decision boundaries without requiring access to the original source data labels.
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3.2.2 Target Classifier Training.
In the second step, we instantiate a new target classificr dh : RD + RK that accommodates both known and unknown classes. The weights for the known classes D6 are initialized using the source classifier's weights gg. while the weights for the unknown classes 9 -
randomly initialized.
The target classifier is trained using the synthetic features and their corresponding labels from the previous step. For known classes, we use pairs (Zenown Bkenown) where Sknown E (l...., K.. and for unknown classes, use pairs (K V(nk)) where Yunk E (K + +I,..., K K").
We optimize the classifier parameters through standard enossyyntropy minimization:
min (cr(sb(z"))"""
where n represents all synthetic features and y their corresponding labels. Figure Siguureillsstatess the resulting decision boundarics that separate both known and unknown classes.
3.2.3 Target Domain Adaptation.
Any source-free unsupervised domain adaptation method (originally designed for closed-set scenarios) be integrated into approach to address open-set scenarios, provided it incorporates diversity loss similar mechanism to facilitate self-learning of unknown classes. To empirically validate this hypothesis, we consider SHOT (Liang et al. 2020) and AaD (Yang et al. 2022), using their respective training objectives for adaptation. SHOT (Liang et al.) 2020 employs information maximization and self-supervised pseudo- labeling adapt the source model the larget domain. Its objective function can be expressed as:
K-K
K+K' K'
Apnnt
Lahot
Ph.i Pog + Adiv
Σ
Phe lolog + Aps E.peuuos
(4)
k=1
where Pk = = Σ. pk (z;;0), and Cpeeudo is the pseudo-labeling loss function from Liang et al. (2020) During adaptation, only the feature encoder is updated while the classifier remains frozen. AaD (Yang all 2022 leverages local consistency and global dispersion. The objective function for feature is formulated as:
LAAD, = Σ PTP, +1
Σ p Pm:
(5)
JEC.
where C, represents the local neighborhood of feature and B, is the mini- batch feature not in C, Unlike SHOT, AaD updates the entire model weights during adaptation.
4
Experiments
4.1
Experimental Setup
Datasets: Office-Home (Venkateswara et al. 2017) comprises 65 labeled image categories from four distinct domains: Art (Ar), Clipart (CI), Product (Pr), and Real World (Rw). We designate the first 25 alphabetically ordered categorics as known classos, with the remaining 40 as unknown. Office-31 (Sacnko ee al. 2010) consists of 31 classes across three domains: Amazon (A), DsIr (D), and Webcam (W). We assign the first 10 as known and the last 10 classes unknown. VisDA (Peng et al. 2017) has 12 categories across two domains: Real (R) and Synthetic (S). The first 6 classes are categorized as known and the remaining 6 as unknown.
Evaluation Metrics: To assess model performance, we adopt standard evaluation metrics widely used in previous OSDA studies (Bucci et al.] 2020 Liu al.] 2019 Li et al.] (002)). The Harmonic Open-sct (HOS) accuracy balances performance on known and unknown classes and can be calculated as HOS 2x0S>XUNK XUNK where 0 represents the accuracy of known classes, and UNK denotes the accuracy of +UNK

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Published in Transactions on Machine Learning Research (05/2025)
Table 1: HOS (%) results on Office-31 (ResNet-50). SF denotes source-free methods. AaD-O and SHOT-O
are the adapted open-set methods of AaD and SHOT. RRDA uses standard AaD and SHOT versions.
Office-31
Methods
SF
A2D
A2W
D2A
D2W
W2A
W2D
Avg
CMU X
52.6
55.7
76.5
75.9
65.8
64.7
65.2
DANCE
X 84.9
78.8
79.1
78.8
68.3
78.8
79.8
OSLPP
x 91.5
89.0
79.3
92.3
78.7
93.6
87.4
GATE
X 88.4
86.5
84.2
95.0
86.1
96.7
89.5
ANNA
X 83.8
85.5
82.5
99.5
81.6
98.4
88.6
Source-only
78.2
72.1
44.2
82.2
52.1
88.8
UMAD
88.5
84.4
86.8
95.0
88.2
95.9
89.8
LEAD
84.9
85.1
90.9
94.8
90.3
96.5
90.3
GLC
82.6
74.6
92.6
96.0
91.8
96.1
89.0
AaD-O
82.3
79.0
84.3
93.1
84.8
95.0
AaD RRDA
91.1
94.3
94.1
96.6
94.0
96.2
94.4
+8.8
+15.3
+9.88
+3.55
+9.2
+1.2
+8.0
SHOT-O
89.5
83.0
85,9
91.4
84.0
95.2
88.2
SHOT+ RRDA
/
90.0
92.2
92.6
98.2
91.6
98.2
93.8
10.5
1.2
| 6.7
6.8
17.6
13.0
+5.66
unknown classes. The HOS metric provides comprehensive measure, by equally weighting the model's ability to classify known classes and detect unknown classes.
Implementation Details: All experiments are conducted on a single A100 GPU using PyTorch. For synthetic data generation, we employ the Adam optimizer with a learning rate of 0.001 for 1000 steps, for both known (zz') and unknown (z classes. In all main experiments, we set K' K. To maintain class balance, we cap the sample size at 1000 for known classes in Office-Home and Office-31, and 10, 000 for VisDA, The target classifier trained for 50 epochs using SGD with a learning rate of 0.01, momentum of 0.9, weight decay of 0.001, and fixed batch size of 128. During larget model adaptation, we use SGD with momentum 0.9, batch size of 64, and train for 50 epochs. The learning rate is set to 0.001 for Office-31 and Office-Home, and 0.0001 for VisDA, when using Resnet-50 (He et al. 2016) as the backbone. When using ViT-B (Wu et al. 2020 as the backbone, we set the learning rate to 0.0001 for all experiments. For SHOT, we freeze the target classifier and train only the feature extractor and backbone. For AaD, all model parameters are trained, with the feature extractor's learning rate set to 10 times lower. During inference, samples belonging to the new K' classes are considered unknown target samples. Aps was set to 0.1, 0.3, and 0.4 for Office-Home, Office-31, and VisDA respectively. The hyper-parameters Adiv and À were set to 1 and Aent set to 0.5 in all our experiments.
During inference, samples assigned to any of the new K' classes are treated as unknown target samples. Following the standard open-set protocol, predictions in the range [1, K] correspond to known classes, while predictions in [K- + K + K'] are aggregated into a single K + class, representing the unknown class.
Baselines: We compare our method against open-set domain adaptation approaches, including both non- source-free and source-free methods. The non-source-free methods include OSBP (Saito et al. 2018), CMU
DANCE (Saito et al.) 2020 GATE (Chen et al. 2022 ANNA (Li et al. 2023) and OSLPP (aang el al. 2024a). For source-free free methods, we consider UMAD [iang el. al.
Fu et al. 2020), STA (Luu et al.] 2019
2021
GLC (Qu et al. 2023), SF-PGL
Luo et al. 2023). and LEAD (Qu et al. 2024).
4.2
Experimental Results
From Table ! to 3 we compare our method against state-of-the-ar (SOTA) OSDA methods in both source- free and non-souree-free setups. We include non-SF methods to provide a comprehensive performance bench- mark, despite our focus on SF scenarios. We use RRDA alongside AaD and SHOT (vanilla methods for closed-set scenarios), well their open-set variants denoted AaD-O and SHOT-O that rely on entropy-
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Published in Transactions on Machine Learning Research (05/2025)
Table 2: HOS (%) results on Office-Homi (ResNet-50 and ViT). |C,] = 25, |Ct]
65. SF denotes source-free.
Office- Home
Methods
SF
Ar2CI Ar2Pr Ar2Rw C12Ar C12Pr C12Rw Pr2Ar Pr2CI Pr2Rw Rw2Ar Rw2CI Rw2Pr
Avg
CMU X
55.0
57.0
59.0
59.3
58.2
60.6
59.2
51.3
61.2
61.9
53.5
55.3
57.6
DANCE
X
6.5
9.0
9.9
20.4
10.1
9.2
28.1
15.8
12.6
14.2
7.9
13.7
12.9
OSLPP
X
61.0
72.8
74.3
60.9
66.9
70.4
63.6
59.3
74.0
67.2
59.0
74.4
67.0
GATE
X
63.8
70.5
75.8
66.4
67.9
71.7
67.3
61.3
76.0
70.4
61.8
75.4
69.0
ANNA
69.0
73.7
76.8
64.7
73.0
66.5
76.6
71.3
65.7
78.7
70.7
Source-only
46.1
63.3
72.9
42.8
54.0
58.7
47.8
36.1
66.2
60.8
45.3
68.2
55.2
Ros 50
UMAD
59.2
71.8
76.6
63.5
69.0
71.9
62.5
54.6
72.8
66.5
57.9
70.7
66.4
LEAD
60.7
70.8
76.5
61.0
68.6
70.8
65.3
59.8
74.2
64.8
57.7
75.6
67.2
GLC
65.3
74.2
79.0
60.4
71.6
74.7
63.7
63.2
75.8
67.1
64.3
77.8
69.8
AaD-O
58.0
68.2
75.4
58.8
65.7
54.6
52.9
72.3
65.8
56.3
72.2
64.1
AaD + RRDA
61.7
72.8
73.5
59.0
74.9
69.9
59.5
58.3
71.2
64.5
64.8
73.2
66.9
+3.7
+4.6
-1.9
+0.2
+9.2
+0.9
+4.9
+5.44
-1.1
-1.3
+7.7
+1.0
+2.8
SHOT-O
57.2
65.4
69.9
58.1
62.6
64.3
60.5
52.8
71.1
64.4
53.5
40.6
61.9
SHOT + RRDA /
64.6
74.2
77.2
63.1
71.4
71.3
67.7
59.1
76.7
70.2
67.4
76.7
70.0
+7.4
+-8.8
+7..3
+-5.0
4-8.8
+-7.0
+77.2
+-6.3
+-5.6
+-5.8
+13.9
4-36.1
+8.1
Source Source-only
57.1
69.5
79.9
50.2
62.5
66.0
52.2
45.7
75.1
69.3
56.4
73.7
63.1
LEAD
58.6
74.7
82.7
58.9
74.6
74.3
59.0
47.1
78.3
71.9
58.7
77.4
68.0
AaD-O
57.8
74.9
82.7
53.9
68.6
70.8
52.5
45.8
76.8
70.6
58.2
77.7
65.9
AaD + RRDA
67.4
77.2
81.2
71.4
73.9
78.5
74.8
75.0
73.2
+9.6
+1.5
-1.5
+17.5
+-2.9
+-5.3
+-214
+18.0
+1.7
+-1.
+.9.3
-2.7
+7.3
SHOT-O SHOT RRDA V
63.6
73.5
81.7
66.7
69.8
75.5
66.5
56.2
79.0
73.5
62.6
74.6
70.3
68.9
75.0
81.4
71.2
73.8
73.6
71.9
60.4
79.2
76.5
66.2
77.5
73.0
+5.3
+1.5
-0.3
+4.5
+4.0
-2.1
+5.4
+4.2
+0.2
+3.00
+-3.6
+229
+2.7
thresholding during training and inference. Results for comparison methods are sourced from Qu et al. 2024 Li et al. 2023), and the mean HOS is reported.
Office-31. Table presents results on the Offico-31 datasct, where RRDA demonstrates significant im- provements over threshold-based methods. AaD+RRDA achieves an average HOS 94.4%, which is an 8.0% increase over AaD-O. Similarly, SHOT-RRDA reaches 93.8%, representing a 5.6% improvement over SHŌT-O. These results surpass all compared source-free and non-source-free SÔTA methods.
Office- Home. On the Office- Home dataset (Table 2,. RRDA consistently enhances the performance of both AaD-O and SHOT-O across most domain adaptation tasks. AaD+RRDA and SHOT+RRDA show average HOS improvements of +2.8% and +8.1% respectively. SHOT+RRDA achieves competitive 70.0% average HOS, outperforming most methods, inchuding both source-free and non-source-free freee approaches, while falling just slightly short of ANNA a non-source-free adaptation method. A similar observation can be made when using ViT as backbone, where RRDA consistently improves previous methods AaD+RRDA and SHOT+RRDA show average HOS improvements of +7.3% and +2.7% respectively, and surpass the other baselines.
VisDA. On the challenging VisDA datasct (Table 31. RRDA contimues to demonstrate its effectivenoss. AaD+RRDA improves upon AaD-O by +6.0% in HOS, significantly improving unknown sample recogui- tion and overall class accuracy. Significant improvements are observed in classes such as "Bus' (+15.9%) and "Truck" (+20.4%) Similar improvements can be observed when applying RRDA SHOT with an improvement of +22.7% in HOS. We observe that both methods improve over the same class and degrade the performances of the "car" and "motorcycles' classes.
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Published in Transactions on Machine Learning Research (05/2025)
Table 3: Accuracy for each class (%) and HOS (%) results on VisDA (ResNet-50 and ViT), with |Ca = 6,
Cel 12.
VisDA
Methods
Bic
Bus
Car
Mot
Tra
Tru
UNK
HOS
OSBP
35.6
59.8
48.3
76.8
55.5
29.8
81.7
62.7
STA
50.1
69.1
59.7
85.7
84.7
25.1
82.4
71.0
Source-only
7.9
24.9
48.0
6.1
72.7
27.9
LEAD
83.5
65.2
57.7
35.7
82.1
79.5
82.7
74.2
ResNet-50
SF-PGL
91.5
90.1
74.1
90.3
81.9
74.8
72.0
77.4
AaD-O
86.8
69.8
51.5
38.7
84.3
26.0
65.5
62.4
AaD + RRDA
96.0
85.7
34.5
37.6
92.2
46.4
71.7
68.4
19.2
| 15.9
-17.0
-1.1
1.7.
10004
6.22
+-6.0
Shot-O
82.1
67.0
78.6
57.3
72.2
17.9
50.7
56.0
Shot RRDA
88.6
82.2
66.8
47.3
87.2
74.3
83.5
78.7
+15.2
-11.8
-10.0
+15.0
+-56.4
+32.8
+ 222.
Sourcc-only
62.3
17.9
17.7
50.7
0.0
0.6
90.8
39.1
LEAD
87.6
65.3
49.8
30.5
70.9
54.4
98.2
74.3
AaD-O
87.9
77.6
47.7
36.8
61.2
16.6
67.6
60.4
AaD + RRDA
91.1
84.8
39.4
94.2
97.5
81.7
82.9
VIT
100.3
13.5
1/37.1
1.2.6
133.0
1.8.
144.1
12.55
Shot-O
96.7
77.0
80.4
75.9
3.0
4.7
80.1
66.1
Shot + RRDA
95.4
84.8
74.1
48.7
85.1
82.3
79.3
78.9
-1.3
+7.88
-6.3
-27.2
4.8.1
477.6
-0.8
12.8
Table 4: Impact of the variance regularization term on Office-Home dataset. using SHOT+RRDA (HOS %).
Method
Ar2CI Ar2Pr
Ar2Rw Cl2Ar C12Pr C12Rw Pr2Ar Pr2CI Pr2Rw Rw2Ar Rw2CI Rw2Pr
Avg
w/o variance term
59.9
70.1
68.3
60.6
67.5
68.0
58.8
58.4
75.7
56.5
55.9
77.2
64.7
w/ variance term
64.6
74.2
77.2
63.1
71.4
71.3
67.7
59.1
76.7
70.2
67.4
76.7
70.0
+4.7
+4.1
+8,9
+2.5
+3.9
+3.3
+8.9
+0.7
+1.0
+13.7
+11.5
-0.5
+5.3
These results demonstrate RRDA's consistent superiority across various domain adaptation scenarios. Our method significantly improves existing SF-OSDA techniques, as evidenced by the consistent performance gains across all datasets. The key advantage of RRDA lies in its novel approach to handling unknown classes. Unlike previous methods that rely thresholding and discard unknown class data during adaptation, RRDA actively learns the semantics of unknown classes through our adaptive target classifier, which evolves to accommodate the unknown class distribution. Furthermore, the consistent performance gains with both AaD and SHOT demonstrate RRDA's 's versatility. These results underscore the importance of explicitly modeling unknown classes in open-set domain adaptation, rather than treating them as outliers to be discarded.
5 Ablation Study and Sensitivity Analysis
Impact of Variance Term. To evaluate the importance of the variance regularization term in our opti- mization objective, we conducted ablation experiments on the Officc-Home datasct using SHOT+RRDS As shown in Table 4 incorporating the variance term yields an average improvement of +5.3% in HOS across all transfer tasks. The variance term proves especially effective for challenging transfer tasks, with substantial improvements on the most difficult transfers: Rw-Ar (+13.7%) and Rw-CC (+11.5%). This confirms the importance of promoting feature diversity during synthetic sample generation, particularly when adapting from complex source domains like Real World.
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Published in Transactions on Machine Learning Research (05/2025)
Table 5: Sensitivity to entropy thresholds on Office using SHOT+RRDA.
Threshold (low/high)
Metrics
0.10/0.90
0.20/0.80
0.25/0.75
0.30/0.70
0.40/0.60
0.50/0.50
HOS (%)
70.5
71.5
72.6
72.0
72.3
72.1
OS* (%)
88.7
89.4
90.0
89.9
90.0
89.0
UNK (%)
59.6
61.1
62.3
61.4
61.6
61.9
1.0
0.30
0.45
0.23
030
SLTA
0:55
LEAD
AAD-O
0.82
(RDK
0:55
030
078
0.70
12111 14 12101718 12202 122224 Number oftargal clesses (C) for the clsssiirr
Number of Unioows Privale Torgai Clesses
Epochh
(a) Various openness settings
(b) Sensitivity K'
(c) HOS convergence
Figure 3: Scnsitivity analysis Offico-31. (a) Adaptation performance across different openness levels
(average across all transfer lasks). (b) Sensilivity to K' target classifier classes (average across all transfer tasks). (c) HOS curves for the A2W task.
Threshold Sensitivity Analysis. We conducted comprehensive analysis of entropy threshold sensitivity the Office-31 dataset using SHOT+RRDA. These thresholds control the selection criteria for synthetic features, with low /high entropy thresholds determining the inclusion boundaries for known/unkong /unknown class candidates respectively. As shown in Table 5 our approach maintains consistent. performance across various threshold configurations, with the 0.25/0.75 setting achieving optimal balance with the highest HOS of 72.6%. We maintain this threshold setting throughout all experiments to ensure consistency across datasets.
Varying Unknown Classes. We investigated the robustness of our framework against an increased number of unknown private classes, which complicates the distinction between known and unknown classes. We compared our method to LEAD, SHOT-O, and AaD-O the Office-31 dataset. As shown in Figure 3a our RRDA method in combination with SHOT and AaD achieves stable results and consistently outperforms existing approaches. For consistency with our main results, we kept K' fixed at 10.
Sensitivity to K'. Figure 3b shows adaptation performance for different K' values of the target classifier on Office-31 dataset. The performance improves as K' increases, validating the benefit of inheriting class separability knowledge, before eventually reaching a plateau. In fact, K' 15 yields the best results. For the main experiments, reported Office-31 results using K' = 10.
Training Stability. Figure 3c illustrates the training curves for the A2W task on the Office-31 dataset. Our method shows consistent HOS improvement on the test set, with steadily increasing before plateauing, In contrast, AaD-O exhibits unstable training, with noticeable performance fluctuations throughout the training process.
Table 6: Runtime comparison of different methods on Office-31 A2D task.
Computational Overhead. We measured runtime compar- isons on the Office-31 A2D task shown in Table G As shown, IRDA introduces only small computational overhead (approximately 31 seconds, or 13% increasc) while achicving significant. performance gains (+5.6% HOS improvement for SHOT+RRDA over SHOT-O on Office-31). The additional computation time primarily comes from the synthetic data gen- eration and target classifier training stages, which require only small fraction of the total adaptation time.
Method
Runtime
LEAD
3m59s
SHOT
4m00s
SHOT+RRDA
4m31s
AaD
4m01s
AaD+RRDA
4m35s
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Published in Transactions on Machine Learning Research (05/2025)
Cleee
Clss
Class
Cleas
1-SNE Feature
H-NE Feutur
Laverown
SNE Festure
GSNE Foeture
L-NE Festure
(a) Source Ouly
(b) AaD-O
(c) AaD RRDA (Ours)
Figure 4: T-SNE visualization of the pre-classifier feature space for the A2W task on the Office-31 dataset.
Feature Space Visualization. Figure shows t-SNE, embeddings of pre-classifier features for the source- only model, AaD-O, and our method on the A2W task on the Office-31 dataset. The source-only model (Figure 4a cxhibits well-soparated known class clusters but mixes unknown samples with known classes. AaD-O Figure [bb slightly improves unknown separation, but class overlap remains. Our method (Figure 4c) achieves superior separation of known and unknown classes, maintaining tight, well-defined known class clusters while isolating unknown samples. This demonstrates our method's effectiveness in inheriting class separability during adaptation.
6 Conclusion
In this work, we introduce Recall and Refine for Domain Adaptation (RRDA), simple but effective framework for SF-OSDA. RRDA enables the successful adaptation of off-the-shelf source pre-trained models to target domains, effectively addressing both distribution and category shift problems. RRDA achieves this by introducing a new target classifier that aids in classifying and learning the semantics of both known and unknown classes. This approach enables the direct use of cource-free adaptation methods designed for closed- set scenarios in open-set contexts. Extensive experiments three challenging benchmarks demonstrate that RRDA significantly outperforms existing SF-OSDA methods and even surpasses OSDA methods that have access to the source domain. Future work could explore its potential for continuous adaplation in the selup where new classes appear over time.
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Giacomo Zara, Subhankar Roy, Paolo Rota, and Elisa Ricci. Autolabel: Clip-based framework for open-set video domain adaptation. In Proceedings of the IEEE/CV /CV Conference on Computer Vision and Pattern Recognition, pp. 11504 11513, 2023.
Yixin Zhang, Zilei Wang, and Weinan He. Class relationship embedded learning for source-free unsuper- vised domain adaptation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognilion, pp. 7619 7629, 2023.
Jinjing Zhu, Haotian Bai, and Lin Wang. Patch-mix transformer for unsupervised domain adaptation: A game perspective. In Proceedings of the IEEE/CVF Conference Compuler Vision and Pallern Recognition (CVPR), pp. 3561-3571, June 2023.
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Published in Transactions on Machine Learning Research (05/2025)
A
Appendix
This supplementary material contains additional information and experiments to support our main paper on source-free open-set domain adaptation. Specifically, it includes additional experiments on foundation model integration, detailed ablation studies on target classifier initialization, and a future direction section.
B Ablation Study
B.1 Target Classifier Initialization
We present an ablation study comparing the impact of known class initialization on the final performance of RRDA on the Office-31 dataset.
In our approach, first generate synthetic points for both known and unknown classes. These synthetic points are then used to train the target classifier before performing the adaptation. Our main paper uses source initialization for the known classes in the target classifier before this training process.
As shown in Table 7 source initialization of the known classes yields a consistent improvement over random initialization. Importantly, both SHOT+RRDA variants (random and source initialization of the target classifier) significantly outperform the baseline SHOT-O method, with average improvements of 5.0% and 5.6%, respectively.
These findings demonstrate that our synthetic data generation process is robust and effective. Even when the target classifier is randomly initialized before training with the synthetic points, our method achieves strong performance. This underscores the quality the generated synthetic points for both known and unknown classes.
Office-31
Methods
Target Classifier
A2D
A2W
D2A
D2W
W2A
W2D
Avg
SHOT-O
89.5
83.0
85.9
91.4
84.0
95.2
88.2
SHOT+ RRDA Random Initialization
90.0
91.5
92.4
98.0
89.0
98.4
93.2
SHOT+ RRDA
Source Initialization
90.0
92.2
92.6
98.2
91.6
98.2
93.8
Table 7: HOS (%) results on Office-31 (ResNet-50). Comparison of the impact of the target classifier initialization on the final performance.
B.2 Foundation Model Integration
Recent trends in domain adaptation have focused leveraging foundation models to tackle the domain shift problem Tang et al. (224]] Zara et al. (223): Singha et. al. (2023) To demonstrate RRDA's flexibility
and compatibility with modern vision- language models, we conducted additional experiments integrating
our approach with CLIP Radford et al. (2021)
For our experimental setup, we used the Office- Home dataset. We didn't finetune the model, and considered shared classes. Our CLIP configuration included both zero-shot classification using text embeddings of 'a photo of [CLASS]" prompts, and our method initialized with CLIP-ViT-B, image encoder. We augmented this with entropy-based unknown detection (threshold 0.5 log(25)), classes prompts.
considered only the known
For our approach, we leveraged texl-guided synthetic feature generation for known classes, while employing a novel K-means clustering strategy (K'=K) on high-entropy features for unknown modeling. Specifically, after identifying samples with high entropy, we optimizod these features to maximize their entropy further. We then applied K-means clustering to these optimized high-entropy features, creating K' distinct unknown prototype clusters. This approach allows us to model the unknown space with multiple centroids rather than
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Published in Transactions on Machine Learning Research (05/2025)
Table 8: HOS (%) results with foundation model integration on Office- Home.
Method
Product
Real
Art
Clipart
Average
CLIP
50.4
56.0
62.8
61.0
57.6
CLIP RRDA
70.7
73.4
74.1
65.7
71.0
single homogeneous 'unknown' class, enabling more nuanced decision boundaries and better separation between known and unknown samples.
Table presents results across different domains, showing that RRDA significantly enhances CLIP's per- formance for open-set domain adaptation. While zero-shot CLIP provides a strong baseline (57.6% average HOS), RRDA integration improves performance by 13.4 percentage points (71.0% average HOS).
C Future Directions and Broader Impact
RRDA offers a robust framework for SF-OSDA with several promising directions for future enhancement. Our current implementation uses a fixed relationship between known and unknown classes (K'=K) for sim- plicity, which has proven effective across diverse benchmarks. Future work could explore adaptive strategies for determining the optimal K' based on target domain characteristics. The performance depends the quality of synthetic features, where optimizing feature generation techniques could further improve adapta- tion results.
From a broader perspective, RRDA advances privacy-preserving machine learning by enabling effective adaptation without source data access. This has significant. implications for sensitive domains like healtheare and industrial systems, where data sharing is restricted by privacy regulations or proprietary concerns. By improving classification of unknown classes, RRDA also enhances the safety and reliability of deployod systems in open-world environments where novel classes may appear unexpectedly. Additional promising research directions include extending RRDA to continual learning scenarios where unknown classes appear over time, and exploring its application in multi-modal settings to further extend its practical impact across diverse application domains.
17


Reviews:
[{"review_id": "QwWxTT9lXj", "reviewer": "Reviewer_jPFV", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "## Strengths\n-  The introduction of synthetic samples in the feature space, coupled with a learnable method for generating new samples, is a novel approach and can be regarded as a significant contribution to the field of source-free OSDA.\n\n- Additionally, the experiments are comprehensive and sufficiently demonstrate the effectiveness of the proposed method across several conventional benchmarks.\n\n## Weaknesses\n- With the rapid development of modern foundation models, conventional model adaptation methods may lack broader insights, as they are often sensitive to data characteristics. It would be interesting to explore how the proposed method could be adapted or applied to foundation models such as DINOv2 or CLIP.\n- The practical challenge of varying ratios of unknown samples across different scenarios remains unaddressed, which is a common limitation shared by existing source-free OSDA methods.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper tackles the source-free open-set domain adaptation (OSDA) problem and proposes a novel method, PRDA, which introduces synthetic features for both known and unknown classes. The proposed PRDA method effectively integrates existing source-free OSDA approaches, and the experimental results demonstrate a significant improvement in performance.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Exploration of Foundation Models: Given the advancements in modern foundation models, it is recommended to investigate how the proposed method could be extended or adapted to work with models such as DINOv2 or CLIP. This would enhance the method's applicability and relevance in the context of current trends in the field.\n\nAddressing Varying Ratios of Unknown Samples: The issue of varying ratios of unknown samples across different practical scenarios is a critical challenge in source-free OSDA. It is suggested that the authors discuss potential strategies or insights to address this limitation, as it is a common weakness shared by existing methods in this domain.\n\nAdditionally, a deeper analysis of the limitations and potential broader impacts of the method would further enhance the paper's rigor and completeness. Overall, this work offers a meaningful contribution to the field of source-free OSDA.", "broader_impact_concerns": "Not available", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "BbxdNUD9eo", "reviewer": "Reviewer_rcMJ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "***Strengths***\n\n1. Novel Approach: The RRDA framework addresses critical gaps in current SF-OSDA methods by focusing on the explicit learning of features for unknown classes, thereby enhancing the model's recognition capabilities.\n\n2. Effective Decision Boundary: The introduction of an additional decision boundary in the target classifier effectively separates known and unknown classes, improving classification accuracy.\n\n3. Flexibility and Applicability: The ability to integrate with various existing source-free domain adaptation methods increases the versatility and practical relevance of the framework in real-world applications, particularly under privacy constraints.\n\n4. Robust Experimental Results: The extensive empirical validation on benchmark datasets solidifies the framework's credibility and establishes a new standard for performance in the field of SF-OSDA.\n\n\n***Weaknesses***\n\n1. How does SF-OSDA differ from traditional out-of-distribution (OOD) detection and open-set domain adaptation (DA) in terms of problem definition, task characteristics, significance, challenges, and real-world applicability, particularly considering that open-set DA also contends with distribution shifts and novel classes? Should state-of-the-art methods from both branches be compared with the proposed framework? Additionally, what evidence supports the claim that the proposed framework can outperform existing approaches?\n\n2. I recall that some open-set domain adaptation studies have explicitly focused on learning features for target-private unknown classes. Additionally, the proposed RRDA appears to be a straightforward combination of OOD detection and SFDA, raising concerns about its technical novelty.The authors may provide further clarification.\n\n3. How should the appended unknown-class target classifier with K' neurons be initialized? How does the random initialization differ from using source-pretrained weights?\n\n4. Figure 1 effectively illustrates the effects and mechanisms of RRDA; however, it lacks a clear depiction of the workflow and network structure, which are crucial for understanding the technical implementation.\n\n5. High entropy with respect to the source classifier does not necessarily indicate unknown classes; it may also reflect hard or uncertain samples from known classes. Given this insight, it may be inappropriate to label this process as \"Synthetic Unknown Classes Generation\".\n\n6. In Eq. (1), what is the underlying principle that dictates the standard deviation of the features of a synthesized sample must be equal to or greater than 1?\n\n7. Due to the thresholding in Synthetic Unknown Classes Generation and Synthetic Known Classes Generation, class imbalance may arise. How does this imbalance affect the final adaptation process? As a potential remedy, could z^t be sampled from a Gaussian distribution and then optimized for high/low entropy and high variance? Additionally, can we design a neural network generator to translate an input z^t to a desired z^{*t} more effectively than through ontology optimization?\n\n8. The authors could enhance their validation by extending it to larger datasets, such as S2RDA [a], and comparing their results to the benchmarks established in that study.\n\n[a] A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation. CVPR, 2023.\n\n9. In Table 4, it appears that diversity has minimal impact on the results. Why?", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "1. Introduction of RRDA Framework: The paper presents Recall and Refine (RRDA), a novel framework for Source-free Open-set Domain Adaptation (SF-OSDA) that explicitly learns discriminative features for target-private unknown classes, addressing significant limitations in existing methods.\n\n2. Two-Step Process: RRDA employs a two-step approach to enhance the model's ability to recognize unknown classes. The first step involves training a target classifier with an additional decision boundary using synthetic samples from target domain features. The second step adapts the entire model to the target domain, addressing domain shifts and improving generalization to unknown classes.\n\n3. Integration with Existing Methods: The framework allows for the seamless integration of various off-the-shelf source-free domain adaptation methods, such as SHOT and AaD, enhancing its applicability across different scenarios.\n\n4. Empirical Validation: Comprehensive experiments conducted on three benchmark datasets demonstrate that RRDA significantly outperforms existing SF-OSDA and OSDA methods, providing strong evidence of its effectiveness.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "See ***Weaknesses***.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "mFqm0CwGgE", "reviewer": "Reviewer_CXK8", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### **Strength**\n- RRDA works in a source-free setting, i.e., it does not require access to the original labeled source data during adaptation. This is especially beneficial when source data cannot be shared due to privacy or legal constraints.\n\n- RRDA explicitly generates synthetic features for both known and unknown classes, which provides potential for better interpretability and differs from conventional methods that rely solely on thresholding prediction entropy to separate known and unknown classes.\n\n- The framework is designed to be compatible with any off-the-shelf source-free domain adaptation method (e.g., SHOT, AaD).\n\n- The framework has been validated on multiple benchmark datasets with comprehensive experiments.\n\n### **Weakness**\n- The paper introduces ambiguous notation and definitions, e.g., the labels are represented with both $I_k$ and $y_k$, making the presentation difficult to understand. Some details are also not clearly elaborated. For example, in the sentence \"we initialize the optimization with a noisy version of the original features $z_t$\", the authors need to clarify what is a noisy version. What noise is added to $z_t$. The original features may point to the features extracted by the source-pre-trained encoder without any optimization, but is not explained here. \n\n- The variance computed in (1) and (2) is not clear. Do authors compute within mini-batches or within the full target dataset? The results could be different and may have different effects. \n\n- The design of the method seems to be heuristic, while the derivation and the rationale behind the choice of the entropy-based loss functions and the accompanying variance regularization terms are not fully explained. \n\n- The optimization in terms of the feature generation is heavily affected by the choice of the threshold, which is jointly decided by the hyperparameter and the number of source classes. The ablation shown in Table 5 is not straightforward to draw the conclusion. The authors should put more discussions on this. \n\n- As RRDA employs a two-stage training process, the authors need to provide an analysis of how synthetic feature generation and the follow-up process increase both computational complexity and training time compared to simpler adaptation methods.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper focuses on the challe of source-free and open-set Domain Adaptation (SF-OSDA), where a  pre-trained model from source domain (labeled data are accessible) needs to be adapted to the target domain data that have both unlabeled and new classes not present in the source. \n\nThe authors introduce a two-step framework called Recall and Refine (RRDA) designed specifically to overcome the above challenges. \n\nIn the first stage, the authors generate synthetic feature points for both known and unknown classes by using the initialized target domain features. For known classes, the optimization is similar to common DA methods, i.e.  minimizing the cross-entropy loss with respect to the source classifier’s prediction. For novel classes, the optimization is guided with a linear combination of entropy and hinge loss, aiming to find out feature points that has entropy greater than a threshold value while avoiding collapse into a degenerated point. \n\nIn the second stage, a new classifier is trained that includes decision boundaries for both the K known classes and the K′ unknown classes. The weights corresponding to the known classes are initialized from the source classifier and the authors use loss functions of the off-the-shelf source-free domain adaptation method (such as SHOT or AaD). \n\nThe framework is evaluated on standard benchmark datasets such as Office-Home, Office-31, and VisDA.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The presentation of the methods part needs to be polished. The analysis of the choice of threshold and computation should be provided. \n\nFor a detailed review, please see the Weaknesses.", "broader_impact_concerns": "No significant concerns and no ethics review needed..", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: All reviewers agree that the current version can be accepted. Thus, no further revision is required at this stage.
Decision: Accept (poster)