Abstract:Training generative adversarial networks (GANs) using limited training data is challenging since the original discriminator is prone to overfitting. The recently proposed label augmentation technique complements categorical data augmentation approaches for discriminator, showing improved data efficiency in training GANs but lacks a theoretical basis. In this paper, we propose a novel regularization approach for the label-augmented discriminator to further improve the data efficiency of training GANs with a the… Show more
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