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Human-designed data augmentation strategies have been replaced byautomatically learned augmentation policy in the past two years. Specifically,recent work has empirically shown that the superior performance of theautomated data augmentation methods stems from increasing the diversity ofaugmented data \cite{autoaug, randaug}. However, two factors regarding thediversity of augmented data are still missing: 1) the explicit definition (andthus measurement) of diversity and 2) the quantifiable relationship betweendiversity and its regularization effects. To bridge this gap, we propose adiversity measure called Variance Diversity and theoretically show that theregularization effect of data augmentation is promised by Variance Diversity.We validate in experiments that the relative gain from automated dataaugmentation in test accuracy is highly correlated to Variance Diversity. Anunsupervised sampling-based framework, \textbf{DivAug}, is designed to directlymaximize Variance Diversity and hence strengthen the regularization effect.Without requiring a separate search process, the performance gain from DivAugis comparable with the state-of-the-art method with better efficiency.Moreover, under the semi-supervised setting, our framework can further improvethe performance of semi-supervised learning algorithms compared to RandAugment,making it highly applicable to real-world problems, where labeled data isscarce. The code is available at\texttt{\url{https://github.com/warai-0toko/DivAug}}.

DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization