Adversarial semantic augmentation estimates feature covariances of real and generated images and optimizes an upper bound of the expected adversarial loss, improving limited-data GAN training without image-level augmentation.
Image Augmentations for GAN Training
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abstract
Data augmentations have been widely studied to improve the accuracy and robustness of classifiers. However, the potential of image augmentation in improving GAN models for image synthesis has not been thoroughly investigated in previous studies. In this work, we systematically study the effectiveness of various existing augmentation techniques for GAN training in a variety of settings. We provide insights and guidelines on how to augment images for both vanilla GANs and GANs with regularizations, improving the fidelity of the generated images substantially. Surprisingly, we find that vanilla GANs attain generation quality on par with recent state-of-the-art results if we use augmentations on both real and generated images. When this GAN training is combined with other augmentation-based regularization techniques, such as contrastive loss and consistency regularization, the augmentations further improve the quality of generated images. We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.
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Adversarial Semantic Augmentation for Training Generative Adversarial Networks under Limited Data
Adversarial semantic augmentation estimates feature covariances of real and generated images and optimizes an upper bound of the expected adversarial loss, improving limited-data GAN training without image-level augmentation.