  Human-designed data augmentation strategies have been replaced by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