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

29 Aug 2018 Xiaofeng Zhang Zhangyang Wang Dong Liu Qing Ling

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... (read more)

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