SelfReg: Self-supervised Contrastive Regularization for Domain Generalization (2104.09841v1)

Abstract: In general, an experimental environment for deep learning assumes that the training and the test dataset are sampled from the same distribution. However, in real-world situations, a difference in the distribution between two datasets, domain shift, may occur, which becomes a major factor impeding the generalization performance of the model. The research field to solve this problem is called domain generalization, and it alleviates the domain shift problem by extracting domain-invariant features explicitly or implicitly. In recent studies, contrastive learning-based domain generalization approaches have been proposed and achieved high performance. These approaches require sampling of the negative data pair. However, the performance of contrastive learning fundamentally depends on quality and quantity of negative data pairs. To address this issue, we propose a new regularization method for domain generalization based on contrastive learning, self-supervised contrastive regularization (SelfReg). The proposed approach use only positive data pairs, thus it resolves various problems caused by negative pair sampling. Moreover, we propose a class-specific domain perturbation layer (CDPL), which makes it possible to effectively apply mixup augmentation even when only positive data pairs are used. The experimental results show that the techniques incorporated by SelfReg contributed to the performance in a compatible manner. In the recent benchmark, DomainBed, the proposed method shows comparable performance to the conventional state-of-the-art alternatives. Codes are available at https://github.com/dnap512/SelfReg.

• The paper introduces a self-supervised contrastive regularization method that uses positive pairs to mitigate instability from negative sampling in domain generalization.
• It integrates a class-specific domain perturbation layer with mixup augmentation, aligning same-class features and stabilizing training.
• Empirical results on PACS and DomainBed benchmarks show that SelfReg achieves robust, competitive accuracy against state-of-the-art methods.

SelfReg: Self-supervised Contrastive Regularization for Domain Generalization

The paper "SelfReg: Self-supervised Contrastive Regularization for Domain Generalization" presents a novel approach to enhancing the robustness of deep learning models in scenarios afflicted by domain shifts, where the distributions between training and testing datasets differ. Addressing the domain generalization (DG) challenge, the authors propose a self-supervised method, named SelfReg, which capitalizes on contrastive learning to derive domain-invariant features without the complications introduced by negative pair sampling.

The essence of SelfReg lies in utilizing only positive data pairs to mitigate domain shifts, resolving the adverse effects and instability that arise from negative pair sampling—an issue present in traditional contrastive learning frameworks. The method employs two core strategies: self-supervised contrastive losses and a class-specific domain perturbation layer (CDPL), often implemented in tandem with mixup augmentation.

Strong Technical Contributions

1. Self-supervised Contrastive Regularization:
   • SelfReg innovatively leverages contrastive learning through positive data pairs alone. This approach circumvents performance degradation often caused by negative sampling—an innovation indicating competitive performance against state-of-the-art methods across various benchmarks.
2. Class-specific Domain Perturbation Layer (CDPL):
   • The introduction of CDPL alongside mixup augmentation ensures that the model effectively interpolates between data samples while preserving class-specific features. CDPL functions as an additional projection layer, akin to an MLP, that stabilizes training by aligning representations of same-class inputs and preventing representation collapse.
3. Gradient Stabilization Techniques:
   • To address potential imbalances in the gradient due to the dominance of the SelfReg loss, stochastic weights averaging (SWA) and inter-domain curriculum learning (IDCL) are employed. SWA, in particular, seeks flatter minima in the loss landscape, enhancing generalizability, while IDCL sequentially integrates domain complexity during training.

Empirical Validation

The efficacy of SelfReg is quantitatively validated using prominent domain generalization datasets, such as PACS and DomainBed, showcasing improvements in terms of both accuracy and stability. Noteworthy results include the SelfReg model achieving the second place in DomainBed’s comprehensive evaluations, with accuracies on par or exceeding several established methods. Under the PACS dataset, SelfReg outperformed previous state-of-the-art, RSC, with notable performance enhancements, verified through extensive ablation studies and supplementary experiments focusing on single-source DG scenarios.

Implications and Future Directions

The development and evaluation of SelfReg offer significant theoretical and practical implications for AI deployments in real-world environments, where unseen data domains are the norm. By efficiently utilizing positive pairs, SelfReg elegantly simplifies the learning process while enhancing robustness against distributional shifts, suggesting potential applications in domains where labeled data from target distributions is scarce or unavailable.

The prospect for refining domain generalization methods through self-supervised learning opens numerous avenues for future research. With the industry’s increasing reliance on AI models performing under uncertain conditions, extensions of SelfReg might explore integrations with advanced architectures, cross-modal data, or environments with dynamic distribution shifts.

Overall, the work on SelfReg represents a substantial contribution to domain generalization strategies, focusing on practical improvements without requiring complex architectural adjustments or reliance on target domain examples during training.