  Deep learning has revolutionized the performance of classification, but
meanwhile demands sufficient labeled data for training. Given insufficient
data, while many techniques have been developed to help combat overfitting, the
challenge remains if one tries to train deep networks, especially in the
ill-posed extremely low data regimes: only a small set of labeled data are
available, and nothing -- including unlabeled data -- else. Such regimes arise
from practical situations where not only data labeling but also data collection
itself is expensive. We propose a deep adversarial data augmentation (DADA)
technique to address the problem, in which we elaborately formulate data
augmentation as a problem of training a class-conditional and supervised
generative adversarial network (GAN). Specifically, a new discriminator loss is
proposed to fit the goal of data augmentation, through which both real and
augmented samples are enforced to contribute to and be consistent in finding
the decision boundaries. Tailored training techniques are developed
accordingly. To quantitatively validate its effectiveness, we first perform
extensive simulations to show that DADA substantially outperforms both
traditional data augmentation and a few GAN-based options. We then extend
experiments to three real-world small labeled datasets where existing data
augmentation and/or transfer learning strategies are either less effective or
infeasible. All results endorse the superior capability of DADA in enhancing
the generalization ability of deep networks trained in practical extremely low
data regimes. Source code is available at
https://github.com/SchafferZhang/DADA.
