  Data augmentation have been intensively used in training deep neural network
to improve the generalization, whether in original space (e.g., image space) or
representation space. Although being successful, the connection between the
synthesized data and the original data is largely ignored in training, without
considering the distribution information that the synthesized samples are
surrounding the original sample in training. Hence, the behavior of the network
is not optimized for this. However, that behavior is crucially important for
generalization, even in the adversarial setting, for the safety of the deep
learning system. In this work, we propose a framework called Stochastic Batch
Augmentation (SBA) to address these problems. SBA stochastically decides
whether to augment at iterations controlled by the batch scheduler and in which
a ''distilled'' dynamic soft label regularization is introduced by
incorporating the similarity in the vicinity distribution respect to raw
samples. The proposed regularization provides direct supervision by the
KL-Divergence between the output soft-max distributions of original and virtual
data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet show that SBA can
improve the generalization of the neural networks and speed up the convergence
of network training.
