Understanding the Detrimental Class-level Effects of Data Augmentation

Polina Kirichenko; Mark Ibrahim; Randall Balestriero; Diane Bouchacourt; Ramakrishna Vedantam; Hamed Firooz; Andrew Gordon Wilson

arXiv · 2401.01764

Understanding the Detrimental Class-level Effects of Data Augmentation

Polina Kirichenko·Mark Ibrahim·Randall Balestriero·Diane Bouchacourt·Ramakrishna Vedantam·Hamed Firooz·Andrew Gordon Wilson

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Abstract

Data augmentation (DA) encodes invariance and provides implicit regularization critical to a model's performance in image classification tasks. However, while DA improves average accuracy, recent studies have shown that its impact can be highly class dependent: achieving optimal average accuracy comes at the cost of significantly hurting individual class accuracy by as much as 20% on ImageNet. There has been little progress in resolving class-level accuracy drops due to a limited understanding of these effects. In this work, we present a framework for understanding how DA interacts with class-level learning dynamics. Using higher-quality multi-label annotations on ImageNet, we systematically categorize the affected classes and find that the majority are inherently ambiguous, co-occur, or involve fine-grained distinctions, while DA controls the model's bias towards one of the closely related classes. While many of the previously reported performance drops are explained by multi-label annotations, our analysis of class confusions reveals other sources of accuracy degradation. We show that simple class-conditional augmentation strategies informed by our framework improve performance on the negatively affected classes.

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Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson. 2023-12-07. Understanding the Detrimental Class-level Effects of Data Augmentation. https://arxiv.org/abs/2401.01764

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