"Data Augmentation Generative Adversarial Networks":
https://arxiv.org/abs/1711.04340v3 #ML #Generative
Abstract: "Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation."