  Data augmentation is a popular technique largely used to enhance the training
of convolutional neural networks. Although many of its benefits are well known
by deep learning researchers and practitioners, its implicit regularization
effects, as compared to popular explicit regularization techniques, such as
weight decay and dropout, remain largely unstudied. As a matter of fact,
convolutional neural networks for image object classification are typically
trained with both data augmentation and explicit regularization, assuming the
benefits of all techniques are complementary. In this paper, we systematically
analyze these techniques through ablation studies of different network
architectures trained with different amounts of training data. Our results
unveil a largely ignored advantage of data augmentation: networks trained with
just data augmentation more easily adapt to different architectures and amount
of training data, as opposed to weight decay and dropout, which require
specific fine-tuning of their hyperparameters.
