Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
Authors: Hung-Chieh Fang
Po-Yi Lu
Hsuan-Tien Lin
Venue: ICML 2025 poster (2025)
Keywords: Domain Adaptation
Universal Domain Adaptation
Machine Learning
Primary area: deep_learning->everything_else

Abstract: Universal Domain Adaptation (UniDA) addresses unsupervised domain adaptation where target classes may differ arbitrarily from source ones, except for a shared subset. A widely used approach, partial domain matching (PDM), aligns only shared classes but struggles in extreme cases where many source classes are absent in the target domain, underperforming the most naive baseline that trains on only source data. In this work, we identify that the failure of PDM for extreme UniDA stems from dimensional collapse (DC) in target representations. To address target DC, we propose to jointly leverage the alignment and uniformity techniques in self-supervised learning on the unlabeled target data to preserve the intrinsic structure of the learned representations. Our experimental results confirm that SSL consistently advances PDM and delivers new state-of-the-art results across a broader benchmark of UniDA scenarios with different portions of shared classes, representing a crucial step toward truly comprehensive UniDA. Project page: https://dc-unida.github.io/

Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
Hlung-Chieh Fang'
Po-Yi Lu
Hsuan- Asuan-Tien Lin
Abstract
Invalid
Universal Domain Adaptation (UniDA) addresses unsupervised domain adaptation where target classes may differ arbitrarily from source ones, except for shared subset. A widely used ap- proach, partial domain matching (PDM), aligns only shared classes but struggles in extreme cases where many source classes are absent in the target domain, underperforming the most naive base- line that trains on only source data. In this work, we identify that the failure of PDM for extreme UniDA stems from dimensional collapse (DC) in target representations. To address target DC, we propose jointly leverage the alignment and uniformity techniques in self-supervised learn- ing on the unlabeled target data to preserve the intrinsic structure of the learned representations. Our experimental results confirm that SSL con- sistently advances PDM and delivers new state- of-the-art results across broader benchmark of UniDA scenarios with different portions of shared classes, representing crucial step to- ward truly comprehensive UniDA. Project page:
Unrealistic
Widely Studied
68-
OfficeHomee
Less Studied
84
O DomainNet
VisDA
62-
Extreme UniDA (high (2))
0.8
The dark region indicates invalid cases with negative shared classes, while the light gray region represents unrealistic scenarios with few or no shared classes. The X-axis indicates Partial DA, and the Y-axis indicates Open-set DA.
Figure I. Spectrum of UniDA:
2018 Jiang et al. 2020). which assumes that the label sets of the source domain (C,) and the target domain (C,) are identical (C, = C.).
More flexible setups such Open-set Domain Adapta- tion (C, C,) and Partial Domain Adaptation (C, C₁) have been studied (Panareda Busto & Gall, 2017 Cao et al. 2018). addressing scenarios where C, has more or fewer classes than C. Universal Domain Adaptation (UniDA) (You et al., 2019) further loosens the limit and uni- fies those setups by not assuming any containment relation between C. and C. Specifically, UniDA assumes unknown shared classes overlap between C, and C,, while allowing each set to contain its private classes. The setup is tasked with classifying target-domain examples as either one of the shared classes or as an unseen class over arbitrary divisions of source- surrceeprivate, shared, and target-private label sets.
1. Introduction
While deep learning and machine learning have achieved remarkable success across wide range of tasks, they still struggle when there is a distribution shift between the train- ing and testing data. Unsupervised Domain Adaptation (UDA) (Pan & Yang 2009) addresses this challenge by transferring knowledge from a labeled source domain with known distribution an unlabeled target domain that may follow different distribution. Arguably the simplest UDA setting is closed-set UDA (Ganin et al. 2016 Long et al.
Addressing the gap between two domains in UDA is com- monly approached by combining loss on the labeled source data with another loss domain matching, which aims to align feature distributions between domains to enable effec- tive knowledge transfer (Ganin et al.) 2016 Courty et al. Tzeng et al., Zellnnel et al. 2017)) Jiang et al. Zellinger
'National Taiwan University. Correspondence to: Hung- Chich Fang 0a09900200@csic.ntt...oo cettine@ssi..tu..cu......
Eisuan-Tier Lin
2016
Proceedings of the 4 International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
2020 However, in the UniDA setting, the presence of private classes in both domains, which are unknown dur-

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Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
ing training, poses significant challenge for this approach. Matching feature distributions without separating shared and private classes can cause negative transfer, where knowl- edge transferred from private classes harms performance instead of improving it (Rosenstein et al. 2005).
for PDM's effectiveness. Consequently, negative transfer intensifies, resulting in performance that falls below So.
To address the issue of target DC, we propose incorporating unlabeled target data into training alongside source-labeled data. Instead of employing aseudo-labeling techniques on unlabeled target data, which are inherently susceptible to the DC issue like PDM, we opt to explore the potential of self-suuervisse learning (SSL) as an alternative. Several traditional SSL techniques based on pretext tasks, such as jigsaw puzzles and rotation, have been explored for related UDA problems (Sun et al. 2019: Xu ct al., 2019: Bucci et al. 2019 2021). However, these methods are not de- signed to tackle the DC issue, resulting in poorly expressive representations (Wallace & Hariharan 2020).
To address this issue, Cao et al. (2018) introduced partial domain matching (PDM), which attempts to match only the data corresponding to the shared classes between domains. While ideal PDM can effectively avoid negative transfer, it can be difficult to accurately determine and separate the examples that belong to the shared classes. Previous work has designed importance weight functions based on uncer- tainty measurement ( You et al. 2019; Fu et al. 2020 Lif
shitz& & Wolf 2021] Chen et al. 2022a Chang et al. 2022
or relative distances in the embedding space (Saito et al.
In contrast, we explore different modern SSL techniques that capitalize on two intuitions. First, semantically similar examples should lie closer in the representation space. Sec- ond, examples in the representation space should be spread out, which avoids degenerate solutions of mapping every example to the same representation and tackles DC directly. We adopt the framework of Wang & Isola (2020), demon- strating that the former can be realized by an alignment loss, and the latter can be achieved with a uniformity loss.
2020: Li et al.] | Chen al., 2022b: Lu al.] 2024
to distinguish examples belonging to shared classes. These approaches are further incorporated into domain-matching frameworks like adversarial training (You et al.) 2019: Fu
et al.
2020 Lifshitz& & Wolf 2021) and self-training (Chen
et al.
2022a Chang et al. 2022 Lu et al. 2024).
While PDM methods for UniDA appear promising, they have not been comprehensively tested broad spectrum of UniDA scenarios. In particular, most UniDA works fol- low the experimental protocols established by You et al. (2019) and Fu et al. 2020). as shown in Figure | The pro- tocols examined the region of relatively higher portions of trrget-private classes and lower portions of source- -private classes. Nevertheless, they did not test UniDA methods on high portion of source-private classes or some of the bound- ary cases of partial DA (X-axis) and open-set DA (Y-axis). The cases of higher source pprrvcte portions, named extreme UniDA, are especially challenging yet under-explored by the community. Such scenarios naturally arise in practice when models are pre-trained on large datasets and then adapted to more specialized tasks with fewer categories. In fact, our careful investigation in Figure 2 surprisingly reveals that all SOTA PDM methods for UniDA failed in extreme UniDA, falling behind the simplest baseline of training on source data only (SO). That is, existing UniDA solutions have yet to fully address the challenges of achieving robust performance across arbitrary divisions of label sets.
Our rigorous ablation study on the two SSL losses reveals a novel insight. When no SSL loss is applied to the tar- get data (SO) or when only the alignment loss is used, the DC issue persists, resulting in the worst performance. In contrast, introducing the uniformity loss yields significant improvements in both dimensional richness and UniDA per- formance. Notably, uniformity and alignment losses offer distinct benefits to the UniDA process, and their combina- tion introduces powerful enhancement that systematically strengthens state-of-the-ane PDM methods. Through an ex- tensive range of experiments, we validate the robustness of this enhancement across diverse prior distributions of label sets. Our findings establish new milestone and benchmark for advancing UniDA across all scenarios comprehensively.
Our contributions can be summarized as follows:
1. We are the first to highlight the unnoticed problem of extreme UniDA that prevents the community from solving UniDA comprehensively, promoting a new re- search direction for the community.
In this work, we attempt to fully achieve comprehensive UniDA by delving into the question: What caused the perfor- mance degrude of PDM in extreme UniDA? We observe that the abundance of source-private classes in extreme UniDA, combined with the structural disparity between source and target data when the model is trained exclusively on source- labeled data, causes target representations to experience dimensional collapse (DC) (Ting et al. 2022). This col- lapse degrades the quality of target representations, which in turn weakens the importance weight functions essential
2. We identify the DC issue behind state-of-the-an PDM methods with careful ablation study and analysis, of- fering novel and fundamental understanding on the (extreme) UniDA problem.
3.
We effectively resolve the DC issue by integrating un- derexplored SSL techniques into UniDA, establish- ing new SOTA performance on more comprehensive benchmarks that cover all UniDA scenarios.
2

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Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
2. Preliminaries
min-max optimization problem for domain matching:
Universal Domain Adaptation (UniDA) You et al.
2019
min max (0;: 0n) Aady Cadv (O5,
(3)
comes with labeled source dataset D, (xx] Mr v
6y.0. 0a
and an unlabeled target dataset D, = [x] Each x represents input vector, and y denotes discrete label.
where And the weighted hyperparameter.
The datasets D, and D, are sampled from some unknown source and target distributions Pa and pt, respectively. We denote the label sets in the source and target domains by Cg and C. Their intersection = C. n C, representing the shared label set. The source-private and target-private label sets are defined C. C. \ and C. = C. \ C. respec- tively. The task of UniDA is to learn feature extractor f and label classifier 0g that classify target data to |C] + classes, where prgett-priaate classes are regarded as one un- seen class. UniDA is particularly challenging as it aims to simultancously achieve high accuracy on shared and unseen target private classes irrespective of the prior distribution of label sets, which can be characterized by the relative propor- tions of the source-private, shared and target-private classes: [2_] [C] [2,] [c,UCT] |C,uc, [c,UC, }. Within the three ratios that nat-
Alternatively, self-training applies cross-entropy loss to high-confidence target samples with pseudo labels:
STt = Eg-m, [x,,(x) 2 8) CE(y. (x)0) (4)
where o is a confidence threshold. The overall objective can be formulated as:
min C. + CsT 0c).
(5)
0y.0c
where ÄSt is the weighted hyperparameter.
Design of importance weight functions. To achieve ro- bust performance in UniDA. it is crucial to design effective importance weight functions, (x)xx and wx(x) to distin- guish shared-class and private-class samples. These func- tions downweight private samples during domain matching by assigning weight of private-class samples and to shared-class samples.
urally sum to 1, the source-private ratio म. = IC,|
[e,uC, and
target-private ratio n, ic,uc, are often taken the key characteristics of the UniDA scenario, as Figure I [showed.
Prior work has explored various approaches to estimate im- portance weights. One common approach uses uncertainty scores derived from the classifier's output (logits) Youu
2.1. Partial Domain Matching (PDM)
In UniDA, the simplest baseline is to train the feature ex- tractor f and label classifier 0g on source labeled data only (SO) with cross-entropy loss:
2019 Fu et al. 2020 Lifshitz & Wolf, 2021| Chang et al. ct al.
2022). leveraging the notion that models trained on
source data will exhibit higher uncertainty when presented with target-private data. However, since classifiers may overfit to classification tasks, alternative line of research leverages distance metrics derived from the feature extrac- tor's 's output (representations) (Saito et al. 2020 Li et al.
(, 0c) -000=EExppp (xx.))<p. CE (((,
To mitigate the distribution shift between source and target domains, domain matching techniques align feature distri- butions across domains. Adversarial training (Ganin et al. 2016) is a prominent approach in the UDA literature and is extensively employed in UniDA You al., 2019 Fu al. 2020 Lifshitz & Wolf 2021). Another widely used method
2021 Chen et al.) 2022a Lu et al. 2024). based on the assumption that shared-class examples lie near each other in the embedding space. While these importance weight functions do offer performance gains, we show that they have inherent limitation, which we discuss in detail in the following section.
in UniDA is self-training (Zou et al.)
2018). which typi-
cally involves assigning pseudo- labels (Chen et al. 2022aa
Lu et al. 2024; Chang et al.) 2022) or leveraging entropy
3. Limitations of PDM
minimization SSitoo et al. 2020).
3.1. Pitfalls of PDM in UniDA
To align feature distributions the adversarial domain matching framework, domain discriminator 0u is intro- duced, and the adversarial loss is formulated as:
The goal of UniDA is to achieve robust performance across arbitrary division of label sets. However, we observe that existing works mainly evaluate a limited range of distribu- tions, neglecting regions with high n,, shown in Figure] Surprisingly, our comprehensive experiments (Figure demonstrate that PDM methods fail to outperform so in high T, region.
Ladv (0, = -Ex~p, (x) log (x))
(2)
Ex we (x) log(1 - Pa(0)(xx))
where < (x),,, (x) S are used to downweight private-class samples. The overall objective involves min- imizing cross-entropy loss on source data and solving a
The inferior performance of PDM approaches suggests that they fail to benefit from the additional domain matching loss
3

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Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
Source Only (SG) UAN (Adversaria Thiiinn! UnioT (SeiFtraining!
Sequer Waar Panc BIRK
Seendee Woke Pork Pole
Figure 3. Singular Value Spectrum Analysis: (a) Spectrum plots the target representations so (C.) under different choices wa (b) Spectrum plots for the target representations derived from C. combined with Lt, across varying amounts labeled target data under n, 0.75. DC is evidenced by certain singular values converging toward zero.
Figure 2. Evaluation of Various PDM Paradigms. We report re- sults on the Officellmme Venkateswaraa et al] 2017) across range of n. values. The star indicates previously unexplored settings.
when Ta high. As discussed in Section 2.1 the success of PDM relies accurately estimating importance weights, which are derived from either logits or representations.
process the quality of the target representations. From Figure 3(b) we observe that training with L, on 10% of the target data significantly mitigates DC, while using 50% of the data achieves performance nearly equivalent to utilizing the entire target dataset. These results suggest that relying solely on L. high n, fails to capture the intrinsic structure of the target data, leading DC.
We hypothesize that, under a high T. regime, training exclu- sively with &, may fail to capture target representations ef- fectively due substantial discrepancies between the souree and target domains. The resulting degraded representations compromise the accuracy of importance weight functions, ultimately impairing overall performance.
3.3. Degraded Representation Quality Impairs PDM
To validate our hypothesis, we examine the target represen- tations from the model trained with L. under varying ws in Section 3.2 Next, in Section 3.3 we analyze how the degraded representation affects the performance of PDM.
In Section 3.2 we demonstrated that the quality of target representations deteriorates as म. increases. Next, we in- vestigate how this degradation affects PDM. As discussed in Section 2. the importance weight function is designed to reduce the influence of private samples during domain matching. Since the weight function is computed based on representations or logits, we hypothesize that the degraded representation quality adversely impacts the effectiveness of the importance weight function.
3.2. Dimensional Collapse in Extreme UniDA
Dimensional Collapse (DC) (nng et al. 2022) refers to the reduction in the effective dimensionality of learned represen- tations, where features collapse onto a lower-dimensional subspace, leading to loss of diversity in the representation space. It is usually analyzed with the singular value spec- trum (Jing et al. 2022 Shi et al. 2022 Zhang et al. 20233 Further details are provided in Appendix A
To investigate this, design metric to evaluate the error rate of the importance weight function in classifying sam- ples as shared or private. For a batch B during training, the error rate is computed as:
To verify that the model trained with C. can significantly affect representation quality in scenarios with high T., we conduct singular value spectrum analysis under different val- ues of .. As illustrated in Figure 3(a) when n increases to 0.6, several singular values approach zero, showing a clas- sic case of dimensional collapse (DC). This effect becomes more pronounced at म. = 0.75, indicating that applying source-supervised loss in high n, scenarios exacerbates DC.
(B
1
Σ H((x) + Hy EC),
(6)
|B]
(X.))E
where f(x) = lw((x) > 0.5), w(x) E {x,(x), wr (x)}
and 0 S w. (x),, wx < 1.
We first examine the emror-rate threshold beyond which PDM provides no improvement over so. To do so, we run a controlled experiment where we assume perfect knowledge of shared and private classes and systematically vary the error rate in importance weights. When n, 0.75 (Fig- ure Aa)). PDM only outperforms SO if the error rate is below 0.2. Conversely, when n, 0.25 (Figure PDM can tolerate higher error rate, remaining beneficial up to around 0.35.
To further validate that the root cause lies in the dominance of source data, leading the model trained with L, to fail in capturing the intrinsic structure of the target represen- tation, we design an experiment using cross-entropy loss on the target data (L,, assuming target labels are available). Specifically, we gradually reduce the number of labeled in- stances in the target dataset and analyze the impact of this
Next, we investigate how importance weights, estimated ei-

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Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
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4, Sa
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durre trankng
0.61
0.90
2.0
0.82
0.80
0.60
0.95
0.78
5.0
5.0
Insert Heigh: Fundtons
tance Weigh: Functoon
(a) TT = 0.75
(b) TT = 0.75
(c) Tx = 0.25
(d) TT 0.25
Figure 4. Importance Weight Functions Analysis on OfficeHome: (a)(c) show the shared-class accuracy under different Flw (B) compared SD. (b)(d) present the average (1)( under common scoring functions such uncertainty and distance.
ther from uncertainty scores or relative distances, can intro- duce errors in real-world scenarios where the true label set remains unknown. Specifically, we adopt two uncertainty- based methods, UAN (You et al., 2019) and CMU (Fu et al. 2020), which represent, respectively, the first application of uncertainty scores and a subsequent approach that aggre- gates multiple such measures. We also include the energy score (Liu et al. 2020), a highly effective -itt----iittiiutiionn detection method that found yield the best uncertainty employ the
Next, Section 2 we demonstrate how the improved representations benefit PDM. Finally, we present a miniature experiment in Section 4.3 as visualization illustrate the
effects of the proposed loss functions, providing a clearer understanding of their impact.
4.1. The Role of SSL Components in UniDA
In SSL, early work on pretext tasks was driven by the in- tuition that auxiliary tasks can enrich representations by providing diverse learning signals. However, pretext tasks are often limited by task-specific biases, which can hinder the generalization of learned representations (Wallace & Hariharan 2020). We study the more recent methods build the principle that similar samples should share similar representations. A straightforward approach to achieve this is to align the representations of pair of samples generated from the same input x through independent augmentations. The alignment loss can be expressed as:
estimates. For distance-based approaches, L2-distance with a memory bank to compute the importance weights (Saito et al.
2020 Chen et al.) (022aa).
At TT = 0.75 (Figure 4(b). the error rate of estimated weights surpasses w.l- above the 0.2 threshold needed for PDM to excel. In contrast, for म5 0.25 (Figure 4(d)). the error rate is closer to 0.3, which remains below the 0.35 threshold that PDM can handle effectively. The results indicate that when the representation quality substantially degrades, the specific design of importance weight functions plays only a minor role.
CAlign(0)
(7)
4. Tackling Dimensional Collapse with Self-Supervised Learning
where T and T' are independent random augmentation functions. However, optimizing only the alignment loss could also lead to DC (Jing et al. (022)). where the model converges to a trivial solution by collapsing all representa- tions into single point. To avoid this problem, prior work employs various auxiliary objectives, such as contrastive learning (Wang & Isola 2020 Chen et al. 2020), asym-
In this section, we present our approach to mitigating dimen- sional collapse in target representations. As discussed in Section 3.2 incorporating target data can preserve structural information and alleviates DC. To achieve similar results in the absence of labels, we propose leveraging self- self-super learning (SSL). Although SSL has been widely adopted in domain adaptation tasks, including closed-set (Sun et al. Xu et al., 2019) and partial domain adaptation (Bucci ctt al. 2019 2021), existing approaches predominantly rely on pretext tasks, such as jigsaw puzzles and rotation pre- diction (Table []. These methods, however, are neither designed to address DC nor effective tackling it (Wallace & Hariharan 2020). Moreover, they have shown limited ability to generalize to downstream tasks (Teng et al. 2022 Albelwi 2022).
metric models (Grill al. 20200 Chen & He 2021) and
redundancy reduction (Bardes et al., 2021 Zbontar et al. 2021)
2019
To delve into how SSL can be applied to resolve DC, we analyze the approach proposed by Wang Isola (020)). Their method is particularly suitable for analysis because its design can be decoupled from alignment loss, unlike asymmetric models redundancy reduction techniques, which cannot be easily isolated. Wang & Isola (2020) intro- duce the concept of uniformity loss (Wang & Isola 2020
Wang & Liu 2021 Fang et al.) (0024) which encourages the learned representations to be uniformly distributed on the unit hypersphere. It is defined using the average pairwise
In Section 4.1 we explore the usage of more advanced SSL
methods and analyze the role of SSL components in UniDA.
5

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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
Table Comparison of existing SSL approaches UDA. and refer to the source and target domains, respectively, while Cont. stands for contrastive learning.
Sefting
Method
Applied Data
Goal
Sun et al. 2019 Xu el al. (2019)
Closed DA
Pretext
S+T
Minimize domain gap
Bucci al.
2019:200 202
Partial DA
Pretext
T
Learn target info
Ours
UniDA
Cont. & Non-Cont.
Tackle DC
4.2. Improved Representation Quality Enhances PDM
Table Performance comparison on Office31 (A2W) with differ- ent combinations loss functions.
In this section, we demonstrate that enhancing representa- tion quality by SSL can further improve PDM. Figure illustrates the error rate (Eww (Ew((B) (B) of importance weighting in adversarial loss during training. Without incorporating the self-supervised loss, the error rate increases, resulting in performance inferior to SO. In contrast, incorporating the self-supervised loss not only improves representation qual- ity but also reduces the eπror rate. These findings suggest that enhancing representation quality more critical for improving PDM than refining the design of the importance weight functions.
Method
H-score (%, 1)
C.
64.5
L, + Latign
64.6
C. + Lunitoem
65.1
C. + Cxlign LUnifoom
67.2
Gaussian potential (Cohn & Kumar 2007) as follows:
Luniform (0r) = log E
x.x (x0)
where t is fixed hyperparameter.
We study the effect of applying alignment loss and unifor- mity loss in the context of extreme UniDA. As illustrated in Figure and T uniformity loss alone significantly alleviates DC and yields improvements, whereas alignment loss alone fails to address DC and provides little no bene- fit. Combining both losses achieves the best performance. These findings suggest that preserving the intrinsic structure of the target data through uniformity loss plays a critical role in extreme UniDA. Additionally, maintaining feature in- variance via alignment loss further enhances the intra-class relationships, leading to improved overall performance.
Ease Le Casy Esp.
Figure 6. Analysis of Error Rate in Importance Weight Func- tion: The error rate decreases when SSL applied.
4.3. Miniature Experiment Analysis
Log of Singular Values
-10
To further study the effect different loss functions under varying Tas we conduct miniature experiment to visualize the impact. Inspired by Liu et al.) (2022), we generate a 2D dataset simulating scenarios with different values of n, as illustrated in Figure 7(a) and 7(c) This experiment aims to illustrate two key points: (1) the DC problem encountered
-20
Ce Cy Caige
-30
Ey + Cartom
E, + Exigo Euriton
30x
150
when training with L. in extreme UniDA, as discussed in Section 3.2 and (2) the effect of SSL in tackling DC in extreme UniDA as discussed in Section
Singular Value Rank Index
Figure 5. Singular Value Spectrum Analysis: Spectrum plot of the target representation under т. 0.75 with differrnt loss function combinations. Results show that uniformity loss alone can tackle DC.
The dataset consists of three classes: the blue and green classes represent shared classes, distinguished by shape to indicate their respective domains. The red class represents source- source-private data, and its distribution shape simulates a mixture of nource-private classes, with the numbers indicat- ing their respective class counts. Further details about the settings are provided in Appendix D.5
Beyond the presented framework, we also demonstrate that asymmetric models and redundancy reduction can achieve performance comparable to Wang & Isola (2020). The corresponding results are provided in Appendix B.3
6

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Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
Casa E Seura)
das E Seura
Gax
dosl 2 (o (oapet)
(oxpet)
(a) Data (low T.)
(b) Target Feature (low T.))
(c) Data (high xT))
(d) Target Feature (high sT))
Figure 7. Toy experiment visualization with so under different values of T.. Circles and crosses represent source and target data, respectively. The number of red points simulates the "extremity" in universal domain adaptation. (a)(c) show the original data. (b)d) show the target features.
das
Gas
dam
das
(a) L.
(b) La Lalign
(c) La Luntorr
(d) L. Lalign + Luniform
Figure& 8. experiment visualization with different SSL components under different values of Tet All images show target features.
Figures 7(b) and 7 present the results of training with L... The representations exhibit a collapse into line under high T., whereas they retain their structure under low T.. To fur- ther study the effect of different components of SSL in high n, scenarios, we visualize the results in Figure The Cxlign term encourages tighter clustering of representations but exacerbates collapse. On the other hand, Luiifor mitigates collapse at the expense of weaker within-class aggregation. Combining both terms offers a balanced solution, preserv- ing structural distinctions while still promoting meaningful within-class clustering.
Baselines. We considered methods from various domain- matching frameworks and different importance weight func- tions. Specifically, UAN and CMU follow adversarial learning paradigms, while UniOT, DANCE, and MLNet adopt self- training strategies. Further details on these methods are provided in Appendix C.I
Extreme UniDA setting. Considering the challenges of evaluating all methods across every possible label set divi- sion on all datasets, we revealed a setting with a high na to specifically evaluate the effectiveness of PDM methods. In this setup, TT is set to values greater than 0.65 across all datasets, in contrast to the original settings where n, is below 0.35, Further details about this setting can be found in Appendix D.I
5. Experiments
Our experiments seek to answer the following questions: (1) Can SSL benefit all PDM methods? (2) Can SSL generalize across all scenarios, regardless of the prior distribution of label sets?
5.2. Discussion
Compatibility with different PDM methods. In Tables 3 and (Appendix(B..) we present results for two training paradigms under the extreme UniDA setting (F, > 0.65). Our findings indicate that the proposed approach improves performance in both paradigms, with a more pronounced ef- fect under adversarial training. We attribute this discrepancy to adversarial training's explicit effort to match the distribu- tion of selected features, making it more sensitive to errors in the importance weight function. In contrast, self-training mainly assigns pseudo- to high-confidence samples, resulting in a less pronounced improvement.
5.1. Experimental Setup
Dataset. We present results on four widely used bench- marks: Office31, OfficeHome, VisDA, and DomainNet. De- tails of these datasets can be found in Appendix D.3
Evaluation metric. We adopt H-score (Fu et al. 2020) as the metric, which calculate the harmonic mean of accu- racy on common classes ac and accuracy on target-private (unseen) classes az See Appendix D.2 for details.
7

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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
Table H-score (%. 1) on Office and DomainNet. each column, the values are highlighted in bold. while top value each category is highlighted with underline. IW refers to the calculation of importance weight functions. UM: uncertainty measurement OT:
assignment matrix computed optimal transport; NN: nearest neighbor determined by relative distance in embedding space.
Office
DomainNet
Method
IW
A2D
A2W
D2A
D2W
W2A
W2D
Avg
P2R
R2P
P2S
S2P
R2S
S2R
Avg
Adversarial Training
UAN
You et
2019
UM
24.5
61.8
48.9
64.2
27.9
61.3
48.1
11.9
15.1
14.4
17.2
18.1
11.3
14.6
CMC
(u et al
2020
UM
76.8
63.8
56.1
77.2
66.3
78.2
69.7
30.1
42.4
34.1
24.3
32.2
34.1
32.8
UAN+
SSL
UM
87.4
74.9
72.4
81.3
74.9
87.7
79.8
50.1
39.2
35.9
32.7
34.0
49.8
40.3
Self- Self-Training
Un CT
Chang al.
2022
OT
78.8
67.7
86.1
66.9
83.8
81.0
77.4
38.1
29.8
30.8
29.3
29.1
38.3
32.6
UniOT+
OT
79.8
75.9
77.3
84.1
82.4
80.9
39.6
29.9
33.6
31.4
31.1
40.2
34.3
DANCE
Saito et al.
2020
NN
49.7
47.9
54.9
48.9
55.6
50.9
39.4
3.30
11.8
0.90
7.60
35.3
16.4
MLket
Lu ct al. 2024
NN
51.2
61.9
58.1
79.2
59.5
75.5
64.2
48.0
45.9
47.8
48.5
43.7
53.0
47.8
MLNet+
SSL
NN
48.6
60.5
60.2
80.6
60.7
80.3
65.2
48.8
46.5
50.2
50.6
44.7
52.5
48.9
mwill Jerealssss
24
24
384
18
18
15
32
2.4
36
02-
0.2
[
Rd
(a) Adversarial Training (UAN)
(b) Self-Training (Uni0T)
Figure Improvement SSL on the UniDA Spectrum: shows the effectiveness SSL across different (nz, m.) values within the UniDA spectrum two different training paradigms.
For completeness, also evaluated approach in general UniDA setting (T, < 0.35) in Tables 5 and 6 (both in Appendix B.1. The results suggest that it continues to provide a modest improvement. These findings highlight the compatibility of SSL with various PDM methods across both ends of the UniDA spectrum (T, > 0.65 and n, < 0.35). Moreover, the performance gap between extreme and general settings indicates that SSL primarily addresses the
consistently improves performance across all divisions of label sets, indicating its robustness. Moreover, the improve- ment becomes more pronounced as increases (toward the lower-right region), which corroborate our analysis that SSL. is particularly effective at addressing DC, a challenge that is especially severe high n, scenarios.
6. Conclusion
DC issue, as discussed in the next paragraph.
In this work, we underline extreme UniDA, challeng- ing and under-explored scenario that blocks state-of-the-art PDM methods toward solving UniDA comprehensively. We identify dimensional collapse in target representations as the primary cause of PDM's poor performance, arising when the private source data overwhelms the training process. This collapse subsequently undermines the effectiveness of
Robustness regardless of prior label set distributions In this section, we evaluate the effect of SSL across varying values in the UniDA spectrum. Figure illustrates the re- sults for eight different combinations of [T,, n.). with the corresponding contour plot showing the improvement in- troduced by SSL (AssL.. The results demonstrate that SSL.
8

---

Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
partial domain matching. Motivated by these findings, we propose addressing dimensional collapse from the perspec- tive of target data. To achieve this, we revisit self-supervised learning and uncover the critical role of uniformity in miti- gating dimensional collapse. Our proposed solution is com- patible with various partial domain matching methods and accommodates a broad range of label set distributions.
conference on computer vision (ECCV), pp. 135-150, 2018.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9650-96600 2021.
Acknowledgments
Chang, W., Shi, Y., Tuan, H. D., and Wang, J. Unified optimal transport framework for universal domain adap- tation. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho, K. (eds.), Advances in Neural Information Processing Systems, 2022. URL httph:htt/////t Berumidd-Tann6164 / http:///ppeeeeeeee.eeeee net.
We thank the anonymous reviewers and members of CLLaa for their constructive feedback. This work is supported by the National Science and Technology Council in Taiwan via and NSTC 133-2634-F-002- 008. We thank to National Center for perfopperformecc Computing (NCHC) of National Applied Research Labora- tories (NARLabs) in Taiwan for providing computational and storage resources.
NSTC 1 110-2628-E-000---
Chen, L., Du, Q. Lou, Y., He, J., Bai, T., and Deng, M. Mutual nearest neighbor contrast and hybrid pro- totype self-training for universal domain adaptation. Pro- ceedings of the AAAI Conference on Artificial Intelli- gence, 36(6):6248-66 caai.v36i6.20574, URL /tt. Bedex.php/AAA/I httpp///AA/aartttl///eeee///////
Jun. 2022a. doi: 10.1609/
Impact Statement
This paper provides more comprehensive investigation of domain adaptation, enabling models to generalize across broader range of scenarios. This research does not pose significant societal consequences.
Chen, L., Lou, Y., He, J., Bai. T., and Deng. M. Evidential neighhorhood contrastive learning for universal domain adaptation. Proceedings of the AAAI Conference Arti- ficial Intelliigenne, 36(6):6258-84)) (0006666866666888860 Jun. 2022b. doi: 10.
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Tackling Dimensional Collapse toward Comprchensive Universal Domain Adaptation
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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
A. Additional Related Work
Most related work is discussed in each section, and a more detailed version is provided below.
A.1. Self-Supervised Learning for Domain Adaptation
Self-supervised learning (SSL.) has been widely adopted in various unsupervised domain adaptation (UDA) tasks due to its ability to capture invariant features that help bridge domain gaps. Xu et al. (2019) first demonstrated its effectiveness in unsupervised domain adaptation through simple pretext tasks for object detection and semantic segmentation. Building on this foundation, [Bucci et al. (2019) 2021] extended pretext tasks to partial domain adaptation. However, these methods are not designed to address DC and have been shown exhibit limited expressiveness due to task-specific biases (Wallace & Hariharan] 2020), Our work extends the application of SSL to resolving DC in extreme UniDA by exploring more recent methods, including contrastive (Wang & Isola| 2020 Chen et al. (020) and non non-contrastive approaches (Grill et al. 2020] Chen & He 202T (aardes et al. 2021 Zhontar et al. 2. DANCE (Saito et al. (020) addresses UniDA through clustering based on source data. Unlike DANCE, our approach leverages SSL on target data independently of source data, resulting in significantly improved performance, as shown in Section [
SSL has also been explored for UDA in point cloud tasks, where Achituve et al. (2021) applied deformation reconstruction to align representations across domains.
Beyond UDA, recent studies highlight the benefits of SSL pretraining in settings involving distribution shifts or imbalanced learning. Garg et al.] (2024) showed that combining self-training with contrastive SSL. pretraining outperforms either approach alone. Similarly, Liu et al. (2022) found that SSL enhances representation learning for minority classes, improving robustness in imbalanced learning scenarios.
A.2. Universal Domain Adaptation
Universal domain adaptation (UniDA) is a more general form of UDA that makes no assumptions about the label sets relationship between the source and target domains. To achieve domain matching without the interference of private-class
data. Prior works have leveraged uncertainty measurement (You et al. 2019 Fu et al. 2020; Lifshitz & Wolf 2021 Chen jet al. 2022b), assignment matrices obtained from optimal transport (Chang et al., 2022), and nearest-neighhor methods based on relative distance (Saito et al., 2020 Lu et al., 2024) as importance weight functions to distinguish shared and private classes for domain matching. While these methods focus designing various importance weight functions, work takes different approach. We demonstrate that the unsatisfactory performance of partial domain matching stems from degraded representation quality caused by dimensional collapse, rather than the choice of importance weighting alone.
Another line of research (Saito & Saenko 2021 Hur et al. 2023 Lu et al., 2024) focuses on developing robust open-set classifiers to distinguish between common and private classes in target data. While these methods do not explicitly address domain matching, most of them can be seamlessly integrated into any domain matching framework. With the emergence of more advanced models, Zhu et al. (2023b); Deng & Jia (2023) explore the application of models such as vision transformers and pretrained vision models like DINO (Caron et al. 20211 and CLIP (Radford et al.) [222] to UniDA. There are also works that align with our goal of exploring more realistic or under-explored scenarios in UniDA. [u et al.] (2024) investigates source- free UniDA, where source data is unavailable during adaptation. Zhu et al. (203a) addresses generalized UniDA, which aims to identify novel categories and label distributions in the target domain, utilizing active learning to achieve this objective.
A.3. Dimensional Collapse
Dimensional Collapse (DC) refers to the reduction in the effective dimensionality of learned representations, where features collapse onto a lower-dimensional subspace, leading to a loss of diversity in the representation space. DC has been extensively studied across various domains, including metric learning (Roth et al., 2020), self-supervised learning (Jing et al. 2022), class-incremental learning (Shi al.) 2022), federated learning with heterogeneity (Shi et al., (023), and graph collaborative filterin (Zhang et al., 2023). Previous work primarily employs the singular value spectrum to analyze DC efffecss. This involves computing the covariance matrix of embeddings, applying singular value decomposition, and examining the distribution of singular values (usually in logarithmic scale) to reveal insights into the DC effects.
13

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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
B. Supplementary Experimental Results
B.1. Compatibility with different PDM methods
Tables 3 andd present the results for extreme UniDA settings, while Tables S and summarize the results for general UniDA settings. The resutls indicate SSL can be applied to various PDM methods in both general and extreme UniDA settings.
Table 4. H-score (%, f) on Office-Home and VisDA. For cach column, the best values are highlighted in bold, while the top value in each category is highlighted with underline. IW refers to calculation importance weight functions. UM: uncertainty measurement; OT: assignment matrix computed via optimal transport; NN: nearest neighbor determined by relative distance in the embedding space.
Office-Home
VisDA
Method
IW
Ar2CI
Ar2Pr
Ar2Rw
C12Ar
C12Pr
C12Rw
Pr2Ar
Pr2C1
Pr2Rw
Rw2Ar
Rw2CI
Rw2Pr
Avg
S2R
Adversarial Training
1AN
You ct al
2019
UM
29.9
36.4
14.1
22.4
20.6
16.4
26.4
25.1
27.3
31.3
24.4
35.4
25,8
41.5
CMU
Fa ot atal
2020
UM
38.5
43.5
45.7
41.4
41.2
47,5
46.0
46.6
40.3
41.5
38.5
27.2
41.5
34.1
46.3
89.5
Self- Trainiraining
(Chang al. 2022
27.2
32.3
26.6
28.4
29.9
23.2
31.4
29.3
23.0
35.9
34.3
35.3
29.7
49.9
BSL
OT
32.1
30.3
31.0
29.7
28.9
25.6
32.1
33.9
29.1
36.7
35.3
45.6
32.5
61.1
DANCE Saito et 2020
34.0
55.5
82.6
44.2
60.1
34.4
20.8
61.2
65.7
33.6
61.7
44.6
69.1
eT all
71,2
56.3
75.1
MINet+ 3SL
59.4
71.5
66.7
57.5
60.4
68.8
59.9
64.4
53.3
64.8
80.2
Table H-sscore(%,, Office-Home (5/10/50). For each column, the best values highlighted in bold, while the top value in each category is highlighted with underline. IW refers to the calculation of importance weight functions. UM: uncertainty measurement; OT: assignment matrix computed via optimal transport; NN: nearest neighbor determined by relative distance in the embedding space.
Office- (5/10/50)
Method
IW
Ar2CI
Ar2Pr
Ar2Rw
CI2Ar
C12Hr
C12Rw
Pr2Ar
Pr2CI
Pr2Rw
Rw2Ar
Rw2C1
Rw2Pr
Avg
Adversarial Training
CAN
You al.
2019
UM
51.6
51.7
54.3
61.7
57.6
61.9
50.4
47.6
61.5
62.9
52.6
65.2
56.6
CMU
Fu et
2020
UM
56
56.9
59.2
67.0
64.3
67.8
54.7
51.1
66.4
68.2
$7.9
69.7
61.6
UAN+
SSL
UM
53.8
75.1
83.9
63.2
67
77.6
72.2
55.9
81.6
74.2
55.9
81.6
70.2
Gelf-Training
DANCE
Saito al.
2020
NN
26.7
11.3
18.0
33.2
12.5
14.3
41.6
39,9
33.3
16.3
27.1
25.9
25.0
Uni OT
hang 2022
OT
67.3
80.5
86.0
73.5
77.3
84.3
75.5
63.3
86.0
77.8
65.4
81.9
76.6
UniOT+ SSL
OT
70.1
80.7
87.3
73.8
76.7
84.0
76.1
63.9
86.2
77.4
66.3
83.1
77.1
Table H-score(%, 1) on Office (10/10/11). For cach column, the best values are highlighted in bold, while the top value in cach category is highlighted with underline. IW refers to calculation importance weight functions. UM: uncertainty measurement; OT: assignment matrix computed via optimal transport; NN: nearest neighbor determined by relative distance in the embedding space.
Office (10/10/10)
Method
IW
A2D
A2W
D2A
D2W
W2A
W2D
Avg
Adversarial Learning
UAN
You al. 2019
UM
59.7
58.6
60.1
70.6
60.3
71.4
63.5
CMU
I'u et al. 2020
UM
68.1
67.3
71.4
79.3
72.2
80.4
73.1
UAN+
SSL
UM
85.8
83,5
84.7
96.4
84.2
97.2
88.6
Self- Self-iraining
DANCE
Saito al
2020
NN
72.6
62.4
63.3
76.3
57.4
82.8
Chang et
2012
87.0
98.8
87.6
UnLOT+ SSL
OT
86.6
88.8
90.7
98.2
88.6
96.7
91.6
14

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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
B.2. Results of applying SSL on different partition of target data
Since applying supervised loss to source-private data can have significant negative effects, we investigate whether applying SSL to target-private data has similar impact. In Figure [0] the results indicate that while it does introduce some negative effects, these relatively minor compared to the benefits it provides. This could be attributed to its role in preserving structural information rather than focusing solely on classification like supervised loss (Liu et al. 2022).
Lxev Lass Cady
(G)
56-
H-score
50
0.8
0.6
0.2
Tic [G
[C]
Figure The figure illustrates the performance applying SSL either shared subset target data the entire target data under different values. The results suggest that applying SSL to target-private classes can indeed have negative effect, but this impact relatively minor compared to the overall benefits it provides
B.3. Results of different SSL methods
We discuss three paradigms in SSL: for contrastive learning, we adopt AlignUniform (Wang & Isola 2020); for asymmetric models, we utilize SimSiam (Chen He 2021); and for redundancy reduction methods, we employ Barlow Twins (Zhontar
et al. 202I As shown Table our results indicate that each these frameworks significantly enhances performance.
Table 7. Performance comparison on OfficeHome (P2R) using different SSL methods.
Method
H-score
SO
63.2
27.3
UAN + SimSiam (Chen He. 2021
72.3
UAN + AlignUniform (Wang & Isola 2020)
71.8
UAN Barlow Twins (boonarr el al. 202T
70.6
B.4. Results with error bar
We report the results on Office31 (Saenko al. (2000) based on three runs in Table each using different random seed. The standard deviation values are relatively minor compared to the advantages we observe over prior works. The results indicate that method is stable in repetitive trials.
Table 8. H-score of JAN SSL with error bars 1ffice31.
A2D
A2W
D2A
D2W
W2A
W2D
UAN + SSL
75.6 + 0.7
85.4 2.6
87.1 + 2.6
76.3 + 1.4
72.9 1.3
80.0 + 2.5
15

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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
B.5. Sensitivity of hyperparameters
We evaluated the sensitivity of the weighted hyperparameter ASsL by experimenting values between 0.3 and 0.7. Figure II demonstrates minimal sensitivity to this hyperparameter across three settings in the Office-Home, The evaluations conducted using UAN+SSL.
15
H-score
P2A
R2C
C2P
0.4
Value of
Figure 11. Sensitivity of ASSL.
C. Supplmennaary Background
C.1. Partial Domain Matching Framework
Matching Method
Adversarial Training
Self-Traie Self-Training
Weight Calculation
Uncertainty Measurement
UAN, CMU, SS
Optimal Transport
UniOT DANCE, MINeet
Nearest Neighbor
Table PDM Framework of Different Methods.
We introduce two main frameworks widely used in UniDA literature: adversarial training and self-training.
Adversarial Training. Adversarial training aligns feature distributions across domains by optimizing an adversarial loss, which encourages the feature extractor to generate domain-invariant features that fool a domain discriminator: The adversarial loss is defined follows:
Ladv (0y, 0a) Ex-p, w. (x) log 0a(0)(x)) - Exxp, (x) log(1 0a(0)(x)))
Here, (x) and w. (x) are importance weight functions that downweight private-class samples in the source and target domains, ensuring that only shared-class samples are aligned. In an ideal case, the weight (x.)) and wx(x) should assign 0 to clavate-class samples and 1 to shared-class samples. The overall objective can formulated as:
min max (C)( XCadv (0,, 0a)]
(10)
f 0g
where À is the weighted hyperparameter.
Self- Training. Self-training approaches, unlike adversarial training, do not explicitly align the feature distributions across domains. Instead, they assign pseudo-labels to high-confidence target samples and use these pseudo-labels for training. The self-training loss is defined as:
LST (S, 0.) Ex [l::(1))wxxx)))))))>)>>)>))))<)) 2 8} CIE(O, (x))()x)),
(11)
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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
where 8 is confidence threshold.
The importance weight function wy (x) is used to filter out private-class samples and to prioritize target samples with high confidence for training. The overall objective can be formulated as:
min (-(0))0)) + A 8,
(12)
C.2. Calculation of Importance Weight Function
Let p(y|x) x) represent the predicted probability distribution over the possible classes given an input x. Specifically, plu (x) the probability assigned to class ye for the input æ, where 1,2, K and is the number of classes. In our cases, K [C,l-
Entropy.
The entropy (p) is defined as:
K ( = Σ plu x) log
(13)
Confidence.
The confidence C(x) is defined as the predicted probability for the most likely class:
((x) max p())
(14)
Energy Score.
The energy score Exx) is calculated as:
E(x) - log Σ exp(p()(x))
(15)
Relative Distance. UniDA, shared classes in the source domain are expected to be closer to shared classes in target domain compared to target-private classes. Therefore, we can leverage this relationship distinguish between the different label sets. In this method, clustering is first performed the source data, and the distance from given input the nearest cluster centroid is used to calculate the uncertainty. Let Cj represent the centroid of the j-th cluster, and the uncertainty score (x) is computed as:
((x min d(x, CJ);
(16)
where d a distance metric, such as Euclidean distance. The same process can be applied when the input is from the source domain. Note that the score is updated every steps, as calculate the distances every step costly.
D. Details of Experimental Setup
D.1. Extreme UniDA setting
Table 10. Comparison of general and extreme settings across datasets. The general UniDA setting refers to the conventional setup used in prior works.
General
Extreme
Dataset
C.
C
[C,]
Tx
[,,]
C
[Ce]
Office-31 1 Office-Home Venkateswara al.
Saenko et 2010
10
10
11
0.33
24
5
3
0.75
2017)
5
10
50
0.08
50
10
5
0.77
Visda Peng et al. 2017
3
6
3
0.25
8
2
2
0.67
DomainNet (Peng et al. 2019
50
150
145
0.14
250
50
45
0.72
In Table 10 we provide the details of label set distributions for our extreme settings. Following prior work, the classes in each label set are first sorted alphabetically and then divided into three groups: source-private, common, and target-private.
17

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Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation
D.2. Metries
H-score (Fu et al., 2020 is defined as the the harmonic mean of accuracy on common classes ac and accuracy on target-private (unknown) classes "7,-
H-score = 2 ac Oc,,
oc + dc
D.3. Dataset
Office31 (Saenko et al. 2010) contains 31 classes and three domains: Amazon (A), DSLR (D), and Webcam (W), with total of about 4k images. Office-Home (Venkateswara et al. 2017) has 65 classes and four domains: Art (A), Product (Pr). Clipart (CI), and Realworld (Rw), with approximately 15k images. VisDA (Peng et al. 2017 is larger dataset with 12 classes from two domains: Synthetic and Real images, totaling around 280k images. DomainNet (Peng et al.) 2019), the
largest DA dataset, has 345 classes and six domains, with about 0.6 million images. Following prior works (Fu et al. 2020
Chang et al. 2022 Kundu et al. 2022), we use only three domains: Real (R), Sketch (S), and Painting (P).
D.4. Implementation Details
We use ResNet-50 (He et al. 2016) as the backbone model for all experiments, which is pre-trained on ImageNet (Deng al. 2009). The optimizer, scheduler and learning rate are consistent with You al. (2019). The training steps are 10K for all experiments and the batch size
36 for both domains. The hyperparameters are set as follows: 'Adv 0.5 and AssI. 0.5 for Office-Homm DomainNet and VisDA, and Assı. 0.2 for Office31. We use SimSiam (Chen & He 2021 as self-supervised loss as it does not require negative samples or large batch size. The data augmentation strategy follows the same setup SimSiam.
D.5. Toy Experiment Setup
The dataset comprises three classes: the blue and green classes represent shared classes, differentiated by shape to indicate their respective domains. Specifically, source classes 0, target class O, source class 1, and target class are sampled with means of (0, (), (3, 3), (3, (5) and (6, 2)), respectively. All shared classes have an identity covariance matrix, with 50 samples per class. The red classes represents source- source-private data, sampled with a mean of (-10, -10) and a covariance
5 To simulate different n, values, the number of samples in the source suuce--private class varies: we use
matrix of -
50 samples for low and 2500 samples for high T.. For the feature extractor 0,, we use two-layer linear network with ReLU activation (Agarap 2018), where the output size is 2. The classifier 0g is single linear layer with an output size of 3, used to predict the three source classes.
18


Reviews:
[{"review_id": "IRvsuacJmk", "reviewer": "Reviewer_tc8f", "summary": "This work focuses on the problem of extreme Universal Domain Adaptation (UniDA). Firstly, UniDA considers a domain adaptation problem where a model has to be trained with a labeled source domain and an unlabeled target domain, such that the label sets of source and target domains are disjoint (i.e. some classes are source-private, some are shared, and some are target-private). The extreme UniDA problem considers the cases of having a very large number of source-private classes, i.e. many source classes are absent from the target data. This paper analyzes the partial domain matching (PDM) approaches for UniDA and finds that they fail on extreme UniDA due to dimensional collapse (DC). To address DC, they propose to use an existing SSL method that encourages learned representations to be uniformly distributed on the unit hypersphere, in order to preserve their intrinsic structure. Finally, they perform experiments on extreme UniDA and show that existing approaches can be improved using the SSL method.", "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": "We thank the reviewer for the constructive feedback and are glad our analysis strongly motivates the proposed approach. Below, we address the raised concerns.\n\n\n**Missing references**\n\nWe appreciate the reviewer’s feedback on our discussion of SSL for DA. While prior work primarily employs SSL to minimize domain gap—either through pretext tasks or by aligning low-uncertainty samples with class prototypes [2, 3, 4]—our focus is on preventing dimensional collapse (DC) in extreme UniDA. This different goal leads us to adopt SSL paradigms specifically designed to counteract collapse by enhancing feature diversity. Our methods include both contrastive (AlignUniform) and non-contrastive (SimSiam, Barlow Twins) approaches that promote uniformity and decorrelation to mitigate DC [5], rather than simply using pretext tasks for domain matching. We also present performance comparisons on extreme UniDA in the table below (see \"Comparison with other SSL pretext tasks\" of reviewer yX6d), showing that pretext-based and prototype-alignment methods fall short of the performance achieved by approaches specifically targeting DC.\n\n\nThe above content will be included in Section 4 for a more comprehensive comparison in our latest version. We now discuss how our approach differs from the specific works [1, 3, 4].\n\nKundu et al. [1] propose \"sticker-intervention,\" a pretext task that improves domain preservation over traditional SSL tasks. We will include it in the related work.\n\nZhu et al. [3] propose a framework that is highly similar to DANCE [2], comprising prototype alignment (referred to as adaptive memory in [3]) and an entropy separation module. As [3]'s code is unavailable, we use DANCE as a proxy given their similar design. DANCE struggles in extreme UniDA (see Table 3–5), making it a reasonable reference point. We will include a discussion of [3] in the related work section.\n\nSPA [4] introduces an add-on module that performs adaptation at the mid-level layers, as these layers are shown to exhibit lower negative transfer. Their method leverages a Bag-of-Words-inspired pretext task to learn distinct visual word prototypes and promotes prototype alignment via entropy minimization. Their work mainly improves domain matching, which is orthogonal to our work that tackle DC. As their module is designed to be an add-on, it can serve as a complementary component to our method. Since the code is not publicly available, we currently include their method only in the related work discussion and will add it as a baseline once the code is released.\n\n[1] “Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation”, ECCV 2022\n\n[2] “Universal Domain Adaptation through Self-Supervision”, NeurIPS 2022\n\n[3] “Self-supervised Universal Domain Adaptation with Adaptive Memory Separation”, ICDM 2021\n\n[4] “Subsidiary Prototype Alignment for Universal Domain Adaptation”, NeurIPS 2022\n\n[5] “Rethinking The Uniformity Metric in Self-Supervised Learning”, ICLR 2024\n\n**Report common-class accuracy and target-private accuracy separtely in Figure 2.**\n\nThank you for the suggestion! Due to character limitations, we were unable to include the full tables here, but we summarize the observed trends below:\n\n* H-score: The H-score decreases as $\\pi_s$ increases, as shown in Figure 2. Additionally, UniOT and UAN perform worse than SO under high $\\pi_s$.\n* Common-class accuracy: This also decreases with increasing $\\pi_s$. UniOT performs slightly worse than SO at high $\\pi_s$, while UAN shows a clearly lower performance than both.\n* Target-private accuracy: Accuracy on target-private classes declines as $\\pi_s$ increases for all methods. Both UniOT and UAN yield slightly lower scores than SO.\n\n\n\n\n**While the analysis in Figure 4 is interesting and informative, it would be more complete with the inclusion of more recent methods.**\n\nWe agree that more recent methods should be included. Since LEAD is a source-free method and does not use a domain-matching loss, we have instead included MLNet (AAAI'24), which employs mutual nearest neighbors (MNN) for domain matching. This approach is conceptually similar to our distance-based baseline, but its mutual filtering enforces a stricter alignment.\n\n\n\n* Avg. error rate of importance weight function during training \n\n   | |UAN | CMU | Energy | Distance | MNN |\n   | -   | - | - | - | - | - |\n   | $\\pi_s=0.25$ | 0.29| 0.30     | 0.33     | 0.26 | 0.24 |\n   | $\\pi_s=0.75$ | 0.59| 0.53     | 0.51     | 0.49| 0.41 |\n\n    The MNN baseline shows a significant improvement. However, it is still far from the threshold (0.15–0.2), as shown in Figure 4 (a), required to outperform the Source-Only baseline.\n\n**Is the self-supervised loss from Sec. 4.2 the same as the uniformity loss from Sec. 4.1?**\n\nThe self-supervised loss refers to alignment + uniformity loss, where alignment encourages class-wise aggregation and uniformity prevents DC. We’ll clarify this in the revision.", "claims_and_evidence": "* Fig. 2: It would be good to report common classes accuracy and target-private accuracy separately (apart from the H-score), so we have a clear picture of whether both of these are worse than SO for high $\\pi_s$ or if one or the other is worse. This could also give additional insights into which part of the UniDA algorithm needs to be improved for extreme UniDA.\n\n* Fig. 4 (b, d): The analysis is interesting and informative, but it uses only older works (2022 and before). It would be stronger if the same is evaluated with newer works/methods (like LEAD, Qu et al. CVPR 2024 or another work from 2023-2025).", "methods_and_evaluation_criteria": "Yes, the paper uses standard evaluation criteria similar to prior UniDA works and develops new analysis experiments that also seem to be valid and useful.", "theoretical_claims": "Not applicable", "experimental_designs_or_analyses": "* Overall experimental design seems reasonable and valid. And the analysis experiments added to motivate different ideas in Sec. 3 and 4 make the paper very interesting to read.\n\n* Sec. 4.2: Is the self-supervised loss same as the uniformity loss from Sec. 4.1? If yes, it would be good to use the same notation throughout so that it's easier to follow. If not, then better clarify what self-supervised loss is and how it is different from uniformity loss.", "relation_to_broader_scientific_literature": "* This paper analyzes the failure of existing UniDA methods for the difficult setting of extreme UniDA, which is novel and interesting.\n    * Specifically, they analyze the partial domain matching approaches and find that they fail due to dimensional collapse (DC).\n    * To resolve the DC problem, they re-purpose an existing SSL method (hypersphere-based uniformity loss).\n    * Finally, they show improved results for extreme UniDA by incorporating the SSL method into existing UniDA techniques like UAN, UniOT, and MLNet.\n\n* While UniDA is a more complex and niche setting compared to general DA, it is a more practical setting. Further, the analysis experiments in this paper are quite valuable and interesting to motivate future work.", "essential_references_not_discussed": "* SSL-based UniDA work (Kundu et al. 2022) was only mentioned in experimental settings in Appendix D.3. However, this work designed a self-supervised pretext task specifically for Universal DA and should be discussed and compared with the proposed method. This is also currently missing from Table 1 (comparison of existing SSL approaches for UniDA).\n\n* Another SSL-based UniDA work [W1] propose a self-supervised adaptive memory network with consistency regularization, and should be discussed and compared with the proposed method.\n\n* A highly relevant SSL-based DA work [W2] was not discussed and cited in the paper. It explores the use of existing pretext tasks for DA, highlights their limitations, and proposes a new pretext task specifically designed for closed-set DA. It should be discussed in the related work.\n\n[W1] Zhu et al., “Self-supervised Universal Domain Adaptation with Adaptive Memory Separation”, ICDM 2021\n\n[W2] Kundu et al., “Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation”, ECCV 2022", "other_strengths_and_weaknesses": "* The writing of this paper is solid and the analysis experiments in Sec. 3 and 4 strongly motivate the proposed approach.\n\n* A minor weakness is that the SSL method is not novel and re-purposed from Wang and Isola (2020). However, this work seems to be the first to use it for UniDA.", "other_comments_or_suggestions": "None", "questions_for_authors": "Please address the concerns listed above. Overall, the paper is well-written and well-motivated with good results. However, I have some concerns regarding essential references not being discussed properly in the paper, apart from other minor concerns in the experiments. Hence, my rating is currently “weak accept” but I am willing to update my rating based on the rebuttal.\n\n## Update after rebuttal\n\nI thank the authors for their efforts in the rebuttal. Since my major concerns are resolved, I upgrade my rating to \"accept\".", "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": "o6gusgBv7j", "reviewer": "Reviewer_UDYW", "summary": "The paper investigates the cause of the performance degradation of partial domain matching (PDM) in (extreme) universal domain adaptation (UniDA). Specifically, the paper presents the failure mode of PDM resulting from dimensional collapse (DC) in target representations, in extreme UniDA where the source-private classes are abundant. To address this issue, the paper proposes to incorporate self-supervised learning on unlabeled target data during training. Ablation studies and empirical evaluations are presented.\n\n---\n\n### Post-rebuttal\n\nI find the clarification in authors' rebuttal helpful and encourage authors to incorporate it in manuscript. I have increased my score.", "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": "We would like to thank the reviewer for the constructive feedback. The primary concern raised is whether the utilization of unlabeled target data during training is still consistent with the Extreme UniDA setting. We address this concern below:\n\n**Concern about the utilization of target data, which makes the setting no longer extreme UniDA.**\n\nWe appreciate the reviewer’s perspective and acknowledge that different lines of work may interpret or label these setups differently. However, our approach remains squarely within the unsupervised domain adaptation (UDA) paradigm—specifically, universal domain adaptation (UniDA). In this framework, both label sets (label shift) and underlying data distributions (covariate shift) can differ between source and target domains—aligning with the first category the reviewer mentioned—and having access to unlabeled target data during training is a standard assumption.\n\nWe will clarify in the revised manuscript that having unlabeled target data during training is not an additional assumption outside of domain adaptation; rather, it is intrinsic to UDA and UniDA. Removing all target data during training would place the problem under settings like domain generalization [1], zero-shot transfer [2], or test-time adaptation [3], which focus on generalizing without any prior exposure to the target domain. In contrast, both UDA and UniDA explicitly require access to unlabeled target data, and every baseline PDM method we compare (e.g., UAN, CMU, UniOT) operates under this same premise.\n\nTherefore, while we recognize that terminological nuances may vary, our work consistently follows the established UniDA framework. We do not shift toward a purely supervised or semi-supervised learning problem on the target domain; instead, we remain entirely within the standard assumptions of UniDA by incorporating unlabeled target data during training.\n\n[1] Li et al., “Learning to generalize: Meta-learning for domain generalization”, AAAI 2018\n\n[2] Radford et al., “Learning Transferable Visual Models From Natural Language Supervision”, ICML 2021\n\n[3] Wang et al., “Tent: Fully test-time adaptation by entropy minimization”, ICLR 2021\n\n\n**Interchangeable usage of UniDA and UDA**\n\nThank you for your helpful suggestion. We agree that the interchangeable use of UniDA and UDA may cause confusion. While our primary focus is on Universal Domain Adaptation (UniDA), we include key references from the Unsupervised Domain Adaptation (UDA) literature to provide necessary context. In the revision, we will clarify this distinction and streamline the discussion of UDA works to maintain focus.", "claims_and_evidence": "The primary claim of the paper is that DC results in the failure of PDM in extreme UniDA settings. The evidence comes from the analyses on different applications of SSL loss functions, employing the Wang and Isola (2020) framework.", "methods_and_evaluation_criteria": "The method starts from analyzing SSL loss functions to demonstrate the pitfalls of PDM in UniDA, and followed by incorporating unlabeled target data into training, in addition to source-labeled data.\n\nThe evaluation criteria depend on the data, and include accuracy, H-score (based on accuracy), and various visualizations.", "theoretical_claims": "The evidence of the paper primarily comes from empirical evaluations and illustrations.", "experimental_designs_or_analyses": "The illustrative experiments on the influence of extreme UniDA on PDM, in terms of DC, are based on singular values (consistent with previous approaches), as well as the relation of DC and loss functions (employing particularly the framework of Wang and Isola, 2020).\n\nThe experiments (Section 5) are designed to showcase the potential benefit of SSL for PDM, and the generalization across different DA settings.", "relation_to_broader_scientific_literature": "The paper is related to different settings/instantiations of domain adaptations.", "essential_references_not_discussed": "There are no significant missing references (with the caveat that the setting is sensible, more in Other Strengths and Weakness part).", "other_strengths_and_weaknesses": "I have one particular concern: the utilization of target data (although unlabeled) makes the setting no longer extreme UniDA.\n\nOn the one hand, if the distinction of settings (e.g., UDA or UniDA) is w.r.t. both the label sets (explicitly) and constraint on the corresponding features (implicitly), then the gap \"Extreme UniDA\" in the spectrum of UniDA (Fig. 1) is important and meaningful to address. However, the introduction of target data without labels during training shifts the setting to a learning problem instead of a DA problem. The previous approaches compared are DA methods, and therefore, are not sufficient to demonstrate the benefits of the proposed approach.\n\nOn the other hand, if the distinction of settings is only w.r.t. the labels themselves, then the setting of \"Extreme UniDA\" becomes an ill-posed problem, since there is no additional assumption/leverage on how target features (not labels) relate to the source domain. According to the paper, the appearance of unlabeled data (from target domain) is still within Extreme UniDA.", "other_comments_or_suggestions": "(additional comment on material organization) \n\nIn the Introduction, the settings and approaches for UniDA and UDA are introduced interchangeably. Considering the fact that the abbreviations are very similar, it might worth considering rearranging the material to make the content more consistent locally (e.g., no jumping back and forth between UDA and UniDA, now that the object of interest is UniDA).", "questions_for_authors": "Can authors share further clarifications/discussions w.r.t. the concern about the shift in setting due to the utilization of target data (even if unlabeled) during training?", "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": "dDhAAUYXw8", "reviewer": "Reviewer_yX6d", "summary": "This paper addresses the universal domain adaptation problem, which is useful in real-world applications. They identify the failure of partial domain matching by dimensional collapse and propose to jointly leverage the alignment and uniformity techniques to avoid dimensional collapse. Experiments on four datasets and benchmarks highlight its superior performance.", "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": "Thank you for the constructive feedback. We address the raised concerns below.\n\n**Novelty and contributions**\n\nWhile SSL has been explored in various DA contexts, we respectfully argue that our contribution lies beyond using SSL for UniDA.\n\nPrior SSL for UniDA works typically rely on pretext tasks (e.g., rotation, jigsaw), which are ineffective in mitigating DC and fail under extreme UniDA settings, as shown in our comparison table below (**Comparison with other SSL pretext tasks**). In contrast, we use both contrastive learning and non-contrastive learning methods, which are explicitly designed to preserve representation diversity and combat dimensional collapse (DC) [1]. To the best of our knowledge, we are the first to apply these techniques in UniDA specifically to address DC, making our use of SSL novel in both motivation and application.\n\nWe also highlight that **identifying the previously overlooked challenge of extreme UniDA is itself a key contribution**, as it draws attention to the limitations of current benchmarks and provides a foundation for systematically addressing the problem through improving representation quality.\n\n**Ablations on ViT backbone**\n\nWe initially excluded this analysis because it is not commonly included in UniDA literature, even in recent works (e.g., LEAD, MLNet), except for [2], which specifically investigates ViT architectures. The following tables present the performance of ViT on both extreme UniDA and general UniDA settings for DomainNet. The results follow a similar trend to those observed with CNNs: improvements are more pronounced in the Extreme UniDA setting compared to the standard UniDA case.\n\n* Extreme UniDA\n    |  | P2S | P2R  | S2P | S2R | R2P | R2S | Avg\n    | - | - | - | - | - | - | - | - |\n    | UAN     | 44.69     | 59.18     | 40.96  | 57.44 | 39.70 | 36.07 | 46.34\n    | UAN + SSL     | 51.57     | 61.12     | 48.24 |  58.22 | 42.95  | 37.49 | 49.93 (+3.59)\n\n* General UniDA \n    |  | P2S | P2R  | S2P | S2R | R2P | R2S | Avg |\n    | - | - | - | - | - | - | - | - |\n    | UAN     | 44.38     | 62.58     | 34.11 | 53.31 | 51.34  | 41.62 | 47.89\n    | UAN + SSL     | 46.25     | 62.81     | 38.74 | 54.76 | 53.26 | 42.11 | 49.65 (+1.76)\n    \n**Lack of theoretical insights in support of the proposed method**\n\nWe agree that understanding why SSL mitigates DC in extreme UniDA is a valuable theoretical pursuit. While seminal SSL methods (e.g., SimCLR, SimSiam, Barlow Twins) were initially driven by intuition and empirical success, later works sought theoretical explanations [3, 4]. We believe that our philosophical insights and empirical results lay a strong foundation for future theoretical study.\n\n**Comparison with more recent methods**\n\nThank you for highlighting these baselines. We were aware of them but initially excluded them due to differing settings. LEAD uses a source-free setup without target data during training; we now include its results on DomainNet, where it underperforms compared to several baselines under the extreme UniDA setting. [2] uses a ViT backbone, making it incompatible with our CNN-based setup. It was also excluded in the MLNet (AAAI 2024) paper, and its code is unavailable, preventing reproduction within our limited timeframe.\n\n|  | P2R | R2P | P2S| S2P | R2S| S2R| Avg|  \n| - | - | - | - | - | - | - | - | \n|CMU | 30.1 | 42.4  | 34.1 | 24.3  |32.2 | 34.1 | 32.8 |\n| UniOT     |  38.1 | 29.8 | 30.8 | 29.3 | 29.1|  38.3 | 32.6\n| LEAD     |   17.3| 16.5 | 15.4 | 14.8| 15.8 | 15.3 | 15.9 |\n\n**Comparison with other SSL pretext tasks**\n\nWe compare three different approaches to SSL in DA:\n\n1. pretext tasks: Minimize domain gap via auxiliary tasks like jigsaw puzzles or rotation prediction (Bucci et al., Xu et al.).\n2. prototype alignment: Align target samples to source/target prototypes using entropy minimization (DANCE).\n3. Contrastive & non-contrastive: The focus of our paper—these methods promote representation diversity to avoid collapse, using contrastive losses (AlignUniform) or asymmetric architectures without negatives (SimSiam).\n\n|  | H-score on Office31| \n| - | - |\n| Rotation/Location (Xu et al.)  |   59.7  |\n| Jigsaw Puzzles (Bucci et al.)  |    63.4  |\n| DANCE  |   61.2   | \n| AlignUniform  |  71.8    | \n| SimSiam   |    72.3  | \n\nThis suggests that contrastive and non-contrastive methods, which explicitly tackle DC, are effective in extreme UniDA—unlike previously explored SSL approaches, which struggle in this setting.\n\n**Results on PDA and OSDA**\n\nWe refer the reviewer to the \"Results on PDA and OSDA\" section of Reviewer sBWy.\n\n[1] “Rethinking The Uniformity Metric in Self-Supervised Learning”, ICLR 2024\n\n[2] “Universal Domain Adaptation via Compressive Attention Matching”, ICCV 2023\n\n[3] “Understanding Dimensional Collapse in Contrastive Self-supervised Learning”, ICLR 2022\n\n[4] “How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning”, ICLR 2022", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Traditional evaluation criteria [1,2] in univeral domain adaptation always include evaluations under partial-set domain adaptation (PDA), open-set domain adaptation (OSDA), and open-partial-set domain adaptation (OPDA) settings. However, the author only evaluate under last setting without PDA and OSDA. \n\n[1] Universal domain adaptation through self supervision, NeurIPS 2020\n\n[2] LEAD: Learning Decomposition for Source-free Universal Domain Adaptation, CVPR 2024", "theoretical_claims": "No Theoretical Claims", "experimental_designs_or_analyses": "The experimental designs miss important PDA and OSDA setups.", "relation_to_broader_scientific_literature": "The proposed framework could be potentially helpful to other literature.", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "Strengths\n1. The paper addresses the univeral domain adaptation problem, which is a challenging and practical scenario.\n2. The paper is well written and easy to follow.\n3. The paper provides extensive experiments, showing the effectiveness and versatility of the proposed method.\n\nMajor Weaknesses\n1. The authors only use CNN backbones. More ablations on ViT backbone should be added, as it demonstrates strong generalization and adaptation performances compared with CNNs.\n2. Lack of theoretical insights in support of the proposed method.\n3. Comparison with more recent methods [1,2] should be included.\n4. The novelty is limited. The self-supervised learning and uniformity loss have been widely used in universal domain adaptation.\n\n[1] LEAD: Learning Decomposition for Source-free Universal Domain Adaptation, CVPR 2024\n\n[2] Universal domain adaptation via compressive attention matching, ICCV 2023", "other_comments_or_suggestions": "How about replace SSL with other pretext tasks, such as jigsaw puzzles and rotation?", "questions_for_authors": "What is the performances under PDA and OSDA setups?", "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": "kWbLxzo1PU", "reviewer": "Reviewer_sBWy", "summary": "Universal Domain Adaptation (UniDA) tackles unsupervised domain adaptation where the target domain may contain classes that differ arbitrarily from the source, except for a common subset. A common approach, partial domain matching (PDM), aligns only the shared classes but often fails when many source classes are missing in the target—performing even worse than a simple baseline trained solely on source data. This work reveals that such failure is due to dimensional collapse in the target representations. To remedy this, the authors propose leveraging both alignment and uniformity techniques from modern self-supervised learning (SSL) on the unlabeled target data, preserving the intrinsic structure of the learned features. Experimental results demonstrate that SSL significantly improves PDM, setting new state-of-the-art performance across various UniDA scenarios with different proportions of shared classes, marking an important advance toward comprehensive UniDA", "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": "We thank the reviewer for the constructive feedback and are glad to hear that our research problem resonates with real-world scenarios and that the observed dimensional collapse is found to be intriguing. The reviewer’s main concerns lie in the novelty of using SSL and the lack of experiments on partial domain adaptation (PDA) and open set domain adaptation (OSDA). We address these concerns below.\n\n**Novelty of using SSL to address the dimensional collapse issue.**\n> There are concerns regarding the novelty of using SSL to address the dimensional collapse issue. The authors should further clarify this approach and compare it with previous SSL methods used in domain adaptation.\n\nWe refer the reviewer to the \"Missing References\" section of Reviewer tc8f, where we discuss the distinction between our work and the literature on self-supervised learning for domain adaptation.\n\n\n**Why SSL mitigate DC?**\n> The claims and evidence in general are clear and convincing. But why SSL works in addressing dimension collapse may need more explanation.\n\nExisting insights from the SSL literature offer useful intuitions. Contrastive learning frameworks (e.g., AlignUniform, SimCLR) demonstrate that negative samples and the uniformity loss serve as repulsive forces to the alignment loss, helping to prevent dimensional collapse. On the other hand, non-constrastive methods like SimSiam and Barlow Twins show that using asymmetric architectures and encouraging feature decorrelation can promote diverse representations across dimensions, thereby mitigating collapse. We will incorporate these insights into the latest version of our paper.\n\n**Experiments on PDA and OSDA**\n\nThank you for the suggestions! We have now included the results for PDA and OSDA below.\n\n* PDA (21/10/0, Office31)\n\n|   | A2D | A2W | D2A | D2W | W2A | W2D | Avg |\n| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |\n|Source-Only *| 90.4| 79.3 | 79.3 | 95.9 | 84.3 | 98.1 | 87.8\n| CMU*     | 84.1 | 84.2 | 69.2 | 97.2 | 66.8 | 98.8 | 83.4\n| DANCE* |  77.1 | 71.2 | 83.7 | 94.6 | 92.6 | 96.8 | 86.0\n| UAN | 82.3| 83.1 | 69.8 |  96.6 | 64.2 | 98.4 | 82.4\n| UAN + SSL | 91.1 | 84.6 | 81.2 | 97.8 | 83.6 | 98.6 | 89.5(+7.1) |\n\n*: from \"LEAD: Learning Decomposition for Source-free Universal Domain Adaptation\" CVPR'24\n\nThese results indicate that UAN+SSL significantly outperforms previous methods in PDA settings. Interestingly enough, the scenario corresponds to $\\pi_s=0.68$, which is close to the extreme UniDA setting, and all prior domain-matching methods underperform the SO approach, reinforcing our observation.\n\n* OSDA (0/10/11, Office31)\n\n|   | A2D | A2W | D2A | D2W | W2A | W2D | Avg |\n| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |\n| CMU*     | 70.5     | 71.6     | 81.2 | 80.2 | 70.8 | 70.8 | 74.2 |\n| DANCE* | 74.7 | 82.0 | 82.1 | 68.0 |  82.5 | 52.2 | 73.6 | \n| UAN | 67.8 | 68.4 | 78.8 | 75.4 | 66.3 | 68.2 | 70.8\n| UAN + SSL | 68.1 | 69.2 | 79.3 | 76.1 | 67.4 | 69.3 | 71.6 (+0.8)\n\n*: from \"Subsidiary Prototype Alignment for Universal Domain Adaptation\" NeurIPS'22\n\n\nThe result suggest that incorporating SSL does not significantly affect performance. In the setting where $\\pi_s=0$, the critical challenge lies in identifying target-private classes rather than domain matching, which may explain why SSL does not substantially influence the performance.\n\n\n**Ablaion study is only conducted on a subtask of the small Office31 dataset**\n\n\nWe have included a more comprehensive ablation study on the entire Office-31 dataset, which shows that alignment alone provides limited benefit, uniformity leads to a slight performance improvement, and combining both yields the best results.\n\n\n|   | A2D | A2W | D2A | D2W | W2A | W2D | Avg |\n| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |\n| $\\mathcal{L}_s$     |   70.1   |     64.5 | 62.5 | 68.4 | 59.7 | 63.1 | 64.7 |\n| $\\mathcal{L}_s + \\mathcal{L}\\_{\\text{align}}$    |    71.5  | 64.6     | 60.2 | 67.8 | 59.1 | 64.2 | 64.6 (-0.1)\n| $\\mathcal{L}_s  + \\mathcal{L}\\_{\\text{uniform}}$     | 72.2     | 65.1     |63.3 | 68.8 | 60.8 | 64.7 | 65.8 (+1.1)\n| $\\mathcal{L}_s  + \\mathcal{L}\\_{\\text{align}}  + \\mathcal{L}\\_{\\text{uniform}}$       | 72.0     | 67.2     |64.7 | 70.1 | 62.1 | 64.9 | 66.8 (+2.1)", "claims_and_evidence": "The claims and evidence in general are clear and convincing. But why SSL works in addressing dimension collapse may need more explanation.", "methods_and_evaluation_criteria": "1. There are concerns regarding the novelty of using SSL to address the dimensional collapse issue. The authors should further clarify this approach and compare it with previous SSL methods used in domain adaptation.\n\n2. It would be even better if there were a more detailed explanation—such as a theoretical analysis—of why SSL mitigates dimensional collapse. Currently, the paper demonstrates the effectiveness of SSL through experimental results, but the underlying reasons for its success lack a more compelling, supportive explanation.", "theoretical_claims": "No theoretical claims", "experimental_designs_or_analyses": "1. The experiments need to be further extended. UniDA is essentially a generalization of basic DA paradigms such as PDA, OSDA, and SDA, and Extreme UDA is an even broader generalization with greater challenges. If the proposed method performs well in the more challenging UDA settings, it is also necessary to evaluate it on simpler settings (like those inherent in PDA/OSDA) to demonstrate the method's robustness.\n\n2. The ablation study is somewhat simplistic, as it was only conducted on a subtask of the small Office31 dataset, making it difficult to draw comprehensive conclusions. It is necessary to conduct experiments on a wider range of tasks to better demonstrate the method's effectiveness.", "relation_to_broader_scientific_literature": "It can be applied to addressed dimension collapse problem in many fields.", "essential_references_not_discussed": "UDA is an extension and generalization of previous methods such as PDA, OSDA, and SDA. In the Additional Related Works section, we recommend reviewing these settings as well to ensure the overall completeness of the paper.", "other_strengths_and_weaknesses": "+ This paper explores the extreme UniDA scenario, which is more challenging and rigorous compared to previous UDA approaches. This research problem is more reflective of real-world situations and holds significant practical importance.\n\n+ The observed phenomenon of dimensional collapse is very intriguing and has the potential to inspire further research in the field.\n\n- The primary contribution of this paper is addressing the issue of dimensional collapse using SSL methods. Since SSL has been widely applied in domain adaptation, we suggest that the authors dedicate a section to thoroughly discuss this issue, in order to better emphasize the novelty of their approach.", "other_comments_or_suggestions": "Please see above strengths and weaknesses", "questions_for_authors": "No additional questions.", "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: There are two major concerns. One is regarding some experimental setup and results, and the other is regarding the novelty of the proposed method compared with existing SSL-based domain adaptation methods. During rebuttal, the empirical concern has been addressed, while the technical novelty issue is not addressed (the authors only mentioned they *would* discuss the distinction between their work and existing SSL methods for domain adaptation). This makes this paper a borderline paper.
Decision: Accept (poster)