Variance-Covariance Regularization Improves Representation Learning
We propose a method to regularize the variance and covariance of the activation to improve the performance of transfer learning.
摘要
评审与讨论
This paper proposes to adapt regularization components (variance, covariance) from the VICReg to supervised learning contexts, which aims to encourages the network to learn high-variance, low covariance representations, promoting learning more diverse features. The authors focus on an efficient implementation of VCReg and conduct experiments to show its effectiveness in transfer learning.
优点
- Writing and motivation are generally clear.
- The proposed VCReg makes sense as whitening technique has been proved to enhance feature diversity in SL.
- Extensive experiments are conducted to verify VCReg’s performance in transfer Learning.
缺点
- Lack of novelty and practicality.
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Firstly, most SSL methods, such as VICReg, DINO and so on, have been verified to significantly outperform supervised learning on downstream transfer learning tasks. No matter what components are added, SL consistently focuses on matching label information. Therefore, in contemporary machine learning, SL is more commonly used for fine-tuning specific tasks rather than serving as a pretraining method for obtaining general-purpose representations. If the proposed SL+VCReg can outperform current self-supervised learning methods on downstream tasks, in my view, that would truly represent both novelty and practicality. I suggest that the authors conduct direct comparisons between their SL+VCReg method and leading SSL approaches on downstream transfer learning tasks. This would provide a clearer picture of where the proposed method stands in relation to current best practices.
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Secondly, the author proposes adding regularization components to multiple intermediate layers of the neural network. Although the runtime of each component is similar to that of batch normalization (BN), it still introduces additional computational overhead. Moreover, based on the experimental setup, selecting the coefficients and for the two regularization components across different network architectures is challenging and requires significant cost. However, despite these additional burdens, the performance improvement is not particularly remarkable. These issues make it difficult for this method to have strong prospects in practical applications. The authors should discuss potential strategies for efficiently selecting the and coefficients, such as automated hyperparameter optimization techniques. This would help readers better understand the practical implications of implementing VCReg.
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Some aspects of the method's setup are unclear and lack intuitive analysis. Specifically, how VCReg uses spatial dimensions in its calculations is not well explained—I suggest that the authors provide explicit formulas. Additionally, it is unclear how the method determines which intermediate layers to add the regularization components. The lack of clarification on these critical settings raises concerns about the reproducibility of the method. The authors should provide a step-by-step algorithm or pseudocode for how VCReg is applied, including details on handling spatial dimensions and selecting intermediate layers. This would significantly enhance the reproducibility of the method and clarify its implementation.
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Some of the experimental results raise concerns. CDNV measures the degree of clustering of the features, but the authors applied it to "two unlabeled sets of samples." It is unclear how the authors obtained and processed this data, and the significance of using unlabeled data in a supervised learning context is questionable. In Table 6, the results show that ConvNeXt (VCReg) demonstrates significantly weaker clustering ability compared to the standard ConvNeXt, which does not support the claim that this method prevents neural collapse. Furthermore, in Table 4, the reproduced performance of VICReg is even worse than SimCLR, which contradicts the prior knowledge of self-supervised learning. As a result, I find it difficult to be convinced by the experimental outcomes presented in the paper.
问题
See weaknesses.
We appreciate the detailed feedback and constructive criticism on our paper. Below, we address the key points raised in your review and provide clarifications and additional context to enhance understanding of our work.
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Novelty and Practicality of SL+VCReg: We acknowledge the point regarding the growing preference for self-supervised learning (SSL) methods over supervised learning (SL) in transfer learning scenarios. Our work aims to explore the potential of regularization in SL contexts, specifically to address the challenge of neural collapse and encourage feature diversity in representations.However, in the paper, we do provide results with VICReg and SimCLR, and it shows we achieve better performance for transfer learning with SLL methods.
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Additional Computational Overhead and Hyperparameter Tuning Challenges: The concern about computational overhead and the difficulty of tuning coefficients for regularization components is valid. However, we believe the performance improvement should outweight the overhead introduced.
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Clarifications on Method Setup: We will provides explicit formulas in the revised manuscript to clarify how spatial dimensions are handled. For which intermediate layers to add the regularization components, we did provide some experiments in the appendix to demonstrate how we studied it. And the pseudo code is also provided in the appendix.
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Concerns Regarding Experimental Results: For CDNV results, the neural collapse exactly means that the data are all clustered in to the class center. Using VCReg should reduce the clustering ability, since we reduce the effect of neural collapse. The discrepancy in VICReg's performance likely arises from differences in experimental setups or hyperparameter choices. We just adapt the public available hyperparameters without do any tunning.
This paper proposes VarianceCovariance Regularization (VCReg), a method designed to encourage learning representations with high variance and low covariance. Rather than applying VCReg only to the network’s final representation, the authors integrate it into intermediate layers, leveraging an efficient implementation to ensure minimal computational overhead and enable easy integration into existing workflows. The paper presents extensive experiments on multiple tasks, demonstrating VCReg’s effectiveness across various network architectures.
优点
1.The authors perform extensive experiments across multiple tasks, showcasing the effectiveness of VCReg in diverse settings, including transfer learning for images and videos, long-tailed learning, self-supervised learning, and hierarchical classification. 2.The benefits of VCReg are explored thoroughly in Section 5 empirically, which is both interesting and convincing.
缺点
1.The paper lacks a theoretical explanation for how VCReg improves generalization. For instance, can the authors provide a theoretical analysis of VCReg’s impact on the decision boundary or expected risk? A theoretical grounding would clarify VCReg’s influence on generalization and strengthen the methodology. Relevant references for further grounding could include:
- Empirical Bernstein Bounds and Sample Variance Penalization by Maurer et al., 2009
- Variance-based Regularization with Convex Objectives by Namkoong et al., 2017
- Feature Variance Regularization: A Simple Way to Improve the Generalizability of Neural Networks by Ranran et al., 2020
- PAC-Bayes-Empirical-Bernstein Inequality by Tolstikhin et al., 2013
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The description in Section 3.2 could be clearer, particularly regarding spatial dimensions and covariance calculations. Is covariance calculated for each feature dimension, and if so, why do spatial dimensions influence the results? Providing explicit equations would enhance clarity and improve the understanding.
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Minor typos: in line 076, suppresses -> surpasses.
问题
Please refer to the weakness.
Thank you for your thorough review and constructive comments on our paper. Below, we address the weaknesses and questions you raised, along with clarifications and potential improvements.
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Theoretical Explanation for VCReg’s Effectiveness: We appreciate the references provided and will explore integrating these works to derive connections between variance- and covariance-based regularization and generalization bounds. We will include a theoretical analysis in future revisions that examines VCReg’s influence on generalization, particularly its impact on decision boundaries and expected risk.
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Clarification of Section 3.2: Covariance is indeed calculated for each feature dimension, treating spatial dimensions as part of the feature aggregation. This design choice is motivated by the observation that spatially correlated features can introduce redundancy, which we aim to suppress through VCReg. We will revise Section 3.2 to explicitly include equations illustrating the computation of variance and covariance, detailing how spatial dimensions are factored into the calculations. This should make the section more accessible and alleviate confusion.
Thank you for the response to my review. I appreciate the clarifications you provided. However, I still feel that my concerns regarding the theoretical explanation and the clarity of Section 3.2 have not been fully addressed in the current version of the paper. While I recognize your commitment to addressing these points in future revisions, I believe that the paper, as it stands, would benefit from further refinement and additional work before publication. So I decide to decrease my score to 5. Thank you again for your efforts in responding to my feedback.
The paper introduces Variance-Covariance Regularization (VCReg), adaptation of VICReg designed for supervised learning. By focusing on high-variance, low-covariance representations, VCReg targets improved feature transferability and robustness, specifically addressing issues like gradient starvation and neural collapse which are prevalent in supervised pretraining for transfer learning. The authors apply VCReg not only to final representations but also to intermediate layers, achieving performance improvements across image and video-based transfer learning tasks, class-imbalanced datasets, and hierarchical classification.
优点
- VCReg repurpose the variance and covariance components of VICReg for supervised settings, intent to make transfer learning without dependance on invariance
- Improvements are shown when using supervised pretraining on ImageNet and then transferring to other datasets so empirical evidence of presented task/scenarios is largely convincing.
- presentation of this paper is well-structured with clarity.
缺点
- VICReg's regularization easily integrates with other SSL methods like SimCLR since it operates on dimensions across the batch without interfering with contrastive losses so it should ideally can be integration with SSL and supervised losses also. So Why I see VCReg as an special case of VICReg applied on supervised setting rather than novel approach.
- Invariance component is removed to streamline VCReg for supervised tasks however this could reduce robustness to data variations. Without the invariance regularization, I am curious about VCReg generalization capabilities in scenarios like distribution shift. To counter the question, empirical evidence should justify that.
- Application of VCReg across intermediate layers, along with its specialized backward-pass gradient adjustments, adds substantial computational overhead and results in Appendix A does not justify it entirely.
- Results on combining the VCReg with SSL methods in Table 4 is interesting. Focusing on ImageNet results- How to justify that with SimCLR + VCReg doesn't help however It improves performance when VCReg added with VICReg (VICReg having invariance component). Need to understand reason.
- Could the authors elaborate on how VCReg specifically addresses generalization in context of connection between gradient starvation and transfer learning?
问题
Please refer weakness section and I am waiting to get response on it.
Thank you for your thoughtful review and constructive feedback on our submission. Below, we address the points raised in the Weaknesses and Questions sections to clarify and strengthen our contributions:
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VCReg as a special case of VICReg in supervised settings: You are correct that VCReg can be viewed as an adaptation of VICReg for supervised tasks. However, our primary contribution lies in demonstrating how supervised learning objectives can be enhanced by focusing exclusively on the variance and covariance terms. By decoupling invariance and adapting the loss for supervised settings, we provide a tailored approach for transfer learning that improves feature utility. This adaptation, while appearing straightforward, required novel adjustments to ensure effective integration with supervised tasks.
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Robustness and generalization without invariance regularization: We appreciate your concern regarding invariance regularization, particularly under distribution shifts. While VCReg does not include the invariance term, which is central to robustness in VICReg, we believe the supervised losses will contribute to the robustness. Our results with transform learning are exactly shows that our methods are robust to the distribution shift.
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Computational overhead of intermediate layer regularization: As we stated in the paper, the latency of the regularization during training is similar to batch norm layer, and there is no overhead during the inference. we believe the performance improvement should outweight the overhead introduced.
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Combining VCReg with SSL methods: The results in Table 4 highlight an interesting interaction between VCReg and SSL methods like SimCLR and VICReg. The performance degradation with SimCLR + VCReg likely stems from conflicting objectives between SimCLR's contrastive loss and VCReg's emphasis on variance and covariance. In contrast, VICReg naturally complements VCReg due to its shared variance and covariance regularization terms. This synergy may explain the observed improvement. We agree that further investigation is needed to fully understand these dynamics, and we plan to conduct deeper ablation studies to explore the interplay between VCReg and other SSL frameworks.
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Connection between gradient starvation and transfer learning The gradient starvation will cause the networks only learn the features that are easy to learn and ignores all other feature. However, due to the distribution shift of transfer learning, the ignored features could be important for the downstream tasks. So preventing the gradient starvation will lead to better generalization.
Thank you for responding on concerns and open question however I didn't see substantial reasoning to serve VCReg as sole novel method . Rather than, it is useful special case of VICReg for supervised settings with additional considerable benefits. VCReg share limited scientific contribution and does not stand without VICReg formulation. So I decide to keep my score unchanged.
Inspired by self-supervised learning, this work introduces Variance-Covariance Regularization (VCReg) from VICReg to supervised learning. Reviewers were concerned about novelty, computational overhead, and theoretical analysis. After rebuttal, one reviewer decreased the score. AC agrees that the work needs further refinement and authors can improve the work according to the comments from all reviewers.
审稿人讨论附加意见
After rebuttal, the concerns from Reviewer umKu about novelty were not fully addressed and the reviewer kept the score of 5. Moreover, Reviewer E5Zn was unsatisfied with the rebuttal on theoretical analysis and decreased the score to 5.
Reject