Title: Minimal Semantic Sufficiency Meets Unsupervised Domain Generalization
Venue: NeurIPS 2025
Accepted: yes
Keywords: minimal sufficient representations

semantic learning

unsupervised domain generalization
Abstract: The generalization ability of deep learning has been extensively studied in supervised settings, yet it remains less explored in unsupervised scenarios. Recently, the Unsupervised Domain Generalization (UDG) task has been proposed to enhance the generalization of models trained with prevalent unsupervised learning techniques, such as Self-Supervised Learning (SSL). UDG confronts the challenge of distinguishing semantics from variations without category labels. Although some recent methods have employed domain labels to tackle this issue, such domain labels are often unavailable in real-world contexts. In this paper, we address these limitations by formalizing UDG as the task of learning a Minimal Sufficient Semantic Representation: a representation that (i) preserves all semantic information shared across augmented views (sufficiency), and (ii) maximally removes information irrelevant to semantics (minimality). We theoretically ground these objectives from the perspective of information theory, demonstrating that optimizing representations to achieve sufficiency and minimality directly reduces out-of-distribution risk. Practically, we implement this optimization through Minimal-Sufficient UDG (MS-UDG), a learnable model by integrating (a) an InfoNCE-based objective to achieve sufficiency; (b) two complementary components to promote minimality: a novel semantic-variation disentanglement loss and a reconstruction-based mechanism for capturing adequate variation. Empirically, MS-UDG sets a new state-of-the-art on popular unsupervised domain-generalization benchmarks, consistently outperforming existing SSL and UDG methods, without category or domain labels during representation learning.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript introduces MS-UDG, an unsupervised domain generalization method that learns minimal semantic representations by disentangling semantics from variations without requiring labels. While it shows improved performance over prior SSL and UDG methods, its contributions are largely incremental, with limited novelty. Additionally, inaccuracies in table annotations and weak theoretical insights diminish its overall contribution.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n1. The experimental evaluation is comprehensive, including both quantitative benchmarks and qualitative visualizations. \n2. The paper attempts to provide theoretical foundations for the proposed contrastive learning framework.\n\nWeakness\n1. The manuscript offers limited theoretical novelty, as it relies on the well-established concept of Minimal Sufficient Semantic Representation (Line-145) without introducing new insights. Moreover, information-theoretic frameworks have already been extensively explored in domain generalization [#1,#2,#3,#4]. The proposed method exhibits little distinction from prior work, as it merely applies existing ideas without providing any theoretical advancements.\n2. Compared to existing work [#5], this manuscript primarily proposes incremental improvements, including introducing a mask, applying self-attention, and incorporating additional features for contrastive learning. Overall, the manuscript reads more like an extension or combination of [#5] and [#6] rather than presenting fundamentally new contributions.\n3. In Table 1, under the Label Fraction: 10% setting, MS-UDG achieves the second-best performance in the photo and art domains, yet the authors mark it as the best. Similar inconsistencies appear in other parts of the manuscript, where MS-UDG results are incorrectly highlighted as the top-performing method despite ranking second. These misannotations may mislead readers and should be carefully corrected.\n4. In the ablation study shown in Table 5 for the sketch domain, the performance when all losses are combined is worse than using each loss individually.\n5. In Figure 3, the t-SNE visualization shows that the semantic features from MS-UDG exhibit poor category separability.\n6. It is well known that self-supervised learning (SSL) benefits unsupervised tasks; this is not a novel contribution to the manuscript. The claim of operating without domain and category labels is also expected in the unsupervised setting and should not be regarded as a unique contribution.\n\n[#1] INSURE: an Information theory iNspired diSentanglement and pURification modEl for domain generalization[J]. IEEE Transactions on Image Processing, 2024. \n\n[#2] Rethinking domain generalization: Discriminability and generalizability[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024. \n\n[#3] How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis[J]. IEEE Transactions on Information Theory, 2025. \n\n[#4] Invariant information bottleneck for domain generalization[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022\n\n[#5] Towards unsupervised domain generalization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022\n\n[#6] Rethinking minimal sufficient representation in contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16041–16050, 2022\n\nQUESTIONS:\nPlease refer to the weakness for more questions. I am open to changing the score if the concerns are well addressed.\n\nAdditional question\n\n1. The concepts of sufficiency and minimality are directly adopted from existing work. It remains unclear what novel theoretical or practical contributions this manuscript provides beyond these established definitions.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-27"}

comments: SUMMARY:
Authors propose an Unsupervised Domain Generalization (UDG) algorithm with theoretical analyses from the perspective of information theory. They also conduct extensive experiments to show its effectiveness.

STRENGTHS AND WEAKNESSES:
Strengths:

- There are theoretical justifications for the proposed algorithm.
- Experimental results clearly demonstrate the effectiveness of the proposed algorithm.

Weaknesses:

- Newer and more recent SSL methods could be added as baselines, e.g. MoCo-v3 [1], SMoG [2], ReLICv2 [3], BAM [4]



[1] Chen X, Xie S, He K. An empirical study of training self-supervised vision transformers[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 9640-9649.

[2] Pang B, Zhang Y, Li Y, et al. Unsupervised visual representation learning by synchronous momentum grouping[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 265-282.

[3] Tomasev N, Bica I, McWilliams B, et al. Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?[J]. arXiv preprint arXiv:2201.05119, 2022.

[4] Shalam D, Korman S. Unsupervised Representation Learning by Balanced Self Attention Matching[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 269-285.

QUESTIONS:
Please refer to weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-30

comments: SUMMARY:
This paper explores Unsupervised Domain Generalization (UDG), a task aimed at improving the generalization of unsupervised learning models like Self-Supervised Learning (SSL) without using category labels. While prior UDG methods rely on domain labels to separate semantics from variations, this approach is limited by the unavailability of such labels in real-world scenarios. The authors propose a new framework called Minimal-Sufficient UDG (MS-UDG), which learns semantic representations by optimizing for both sufficiency (retaining shared semantic information) and minimality (removing irrelevant variations). Based on information theory, MS-UDG combines an InfoNCE-based objective with novel components to disentangle semantics and variations, achieving state-of-the-art performance on UDG benchmarks without needing category or domain labels.

STRENGTHS AND WEAKNESSES:
Strengths:

1. This paper is well motivated.

2. The experimental results are good.

3. Overall, the proposed method is simple and effetive.

Weaknesses:

1. Line 166 I(x_1;x_2;T) = I(x_1;x_2;S), what does S stands for? According to the following text, S may stand for semantic. But what does semantic information in X_{ssl} means? SSL should contain no label, so it hard to understand.

2. Line 116~117 downstream supervised data P(X_{sup}, Y_{sup}) is used for fine-tuning? Is it the source domain ine Line 270?

QUESTIONS:
Please refer to the Weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-07-02

comments: SUMMARY:
The paper addresses Unsupervised Domain Generalization (UDG) for image classification by formalizing the task through an information-theoretic framework. The authors propose learning Minimal Sufficient Semantic Representations that capture shared semantic information across multiple augmented views of the same image while eliminating semantically irrelevant information. The approach includes theoretical proof demonstrating that this formulation reduces out-of-distribution risk, which directly addresses the UDG challenge.

STRENGTHS AND WEAKNESSES:
**Strengths:**

- **Writing Quality** - The paper is exceptionally well-written with clear and well-motivated technical components.
- **Comprehensive Evaluation** - The experimental comparison against existing methods is extensive and thorough, providing strong empirical validation.
- **Complete Ablation** - The ablation studies effectively justify the use of both loss components, demonstrating that their combination yields superior performance.
- **Theoretical Foundation** - The paper provides comprehensive theoretical analysis, introducing all necessary concepts regarding minimal sufficient semantic representations before presenting the method and its implementation.

**Weaknesses:**

- **Missing Failure Analysis** - The paper lacks a failure analysis section examining cases where competing methods outperform MS-UDG. Such analysis would provide valuable insights into the method's limitations and help readers understand when and why the approach may not be optimal.

QUESTIONS:
Can you provide failure case analysis explaining why certain competing methods outperform MS-UDG in specific scenarios, and what insights does this offer about your method's limitations ?

LIMITATIONS:
yes.
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-02