Self-Damaging Contrastive Learning (2106.02990v1)

Abstract: The recent breakthrough achieved by contrastive learning accelerates the pace for deploying unsupervised training on real-world data applications. However, unlabeled data in reality is commonly imbalanced and shows a long-tail distribution, and it is unclear how robustly the latest contrastive learning methods could perform in the practical scenario. This paper proposes to explicitly tackle this challenge, via a principled framework called Self-Damaging Contrastive Learning (SDCLR), to automatically balance the representation learning without knowing the classes. Our main inspiration is drawn from the recent finding that deep models have difficult-to-memorize samples, and those may be exposed through network pruning. It is further natural to hypothesize that long-tail samples are also tougher for the model to learn well due to insufficient examples. Hence, the key innovation in SDCLR is to create a dynamic self-competitor model to contrast with the target model, which is a pruned version of the latter. During training, contrasting the two models will lead to adaptive online mining of the most easily forgotten samples for the current target model, and implicitly emphasize them more in the contrastive loss. Extensive experiments across multiple datasets and imbalance settings show that SDCLR significantly improves not only overall accuracies but also balancedness, in terms of linear evaluation on the full-shot and few-shot settings. Our code is available at: https://github.com/VITA-Group/SDCLR.

Self-Damaging Contrastive Learning: An Analysis

The paper "Self-Damaging Contrastive Learning" addresses a significant limitation of current contrastive learning methods when applied to imbalanced, unlabeled datasets that are ubiquitous in real-world scenarios. The authors introduce a framework, Self-Damaging Contrastive Learning (SDCLR), to enhance the robustness of unsupervised contrastive learning methods, mitigating their susceptibility to data imbalance, particularly long-tail distributions.

Problem Context and Research Impetus

Contrastive learning has emerged as a powerful paradigm for unsupervised representation learning, underpinned by the strategy of contrasting positive and negative pairs to learn robust feature representations. While these methods have been empirically successful, they assume a uniform distribution of data, which is seldom the case for real-world data characterized by long-tail distributions. In such distributions, most classes (head classes) contain a plethora of samples, while tail classes have substantially fewer examples, challenging the model's ability to learn adequate representations for the underrepresented tails. This research builds on recent findings about deep neural networks' learning dynamics and aims to incorporate these insights into the development of SDCLR.

Methodological Contributions

SDCLR is predicated on the hypothesis that models minimally memorize long-tail samples due to their few occurrences, which can be leveraged through the lens of model pruning. The framework fundamentally integrates a technique termed "self-damaging," which involves dynamically adjusting the model structure by pruning. The pruning-induced perturbations expose disparities in how different sample groups (head vs. tail) are represented, allowing the model to focus on the more "easily forgotten" long-tail samples.

The implementation of SDCLR involves a dual-branch network design, where one branch (target model) undergoes standard training, while the other branch (self-competitor) is a pruned version of the target network. The two branches are iteratively pruned and retrained, dynamically generating pruning identified exemplars (PIEs) that highlight samples struggling to be adequately represented by the model. These exemplars subsequently guide an implicit rebalancing in the contrastive learning loss function, accentuating the learning of long-tail samples.

Empirical Evaluation

The efficacy of SDCLR is substantiated through comprehensive experiments conducted across several datasets exhibiting long-tail distributions, including CIFAR-10, CIFAR-100, ImageNet-LT, and custom variations of long-tail ImageNet datasets. Notably, SDCLR consistently enhances the linear separability of feature representations and improves few-shot performance, confirming its ability to learn more balanced feature spaces compared to conventional methods. Furthermore, the results demonstrate that SDCLR's structured pruning surpasses random dropout approaches and provides advantages over direct focal loss applications in rebalancing the learning process.

Implications and Future Directions

The implications of this research are significant for the practical deployment of contrastive learning in real-world applications marked by data imbalance. By enabling contrastive models to automatically adjust their focus towards tail samples, SDCLR expands the versatility of unsupervised learning scenarios where labeled data is scarce or imbalanced.

Future developments could extend the applicability of SDCLR beyond image classification to other domains encountering imbalanced data, such as natural language processing and time-series analysis. Additionally, there is scope for integrating SDCLR with other state-of-the-art contrastive learning frameworks to further explore its potential benefits.

In conclusion, SDCLR provides a substantial methodological advancement in unsupervised learning, adeptly addressing data imbalance concerns, and sets a promising stage for further innovations in balanced representation learning.