DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification

Understand

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).