Towards Understanding Why Data Augmentation Improves Generalization
This work addresses a foundational gap in machine learning theory by providing a unified explanation for data augmentation's effectiveness, which is incremental as it builds on existing techniques without introducing new methods.
The paper tackled the problem of understanding why data augmentation improves generalization in deep learning, presenting a unified theoretical framework that explains it through partial semantic feature removal and feature mixing, with experimental results supporting these insights.
Data augmentation is a cornerstone technique in deep learning, widely used to improve model generalization. Traditional methods like random cropping and color jittering, as well as advanced techniques such as CutOut, Mixup, and CutMix, have achieved notable success across various domains. However, the mechanisms by which data augmentation improves generalization remain poorly understood, and existing theoretical analyses typically focus on individual techniques without a unified explanation. In this work, we present a unified theoretical framework that elucidates how data augmentation enhances generalization through two key effects: partial semantic feature removal and feature mixing. Partial semantic feature removal reduces the model's reliance on individual feature, promoting diverse feature learning and better generalization. Feature mixing, by scaling down original semantic features and introducing noise, increases training complexity, driving the model to develop more robust features. Advanced methods like CutMix integrate both effects, achieving complementary benefits. Our theoretical insights are further supported by experimental results, validating the effectiveness of this unified perspective.