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Improved Consistency Regularization for GANs
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Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways. We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then propose several modifications to the consistency regularization procedure designed to improve its performance. We carry out extensive experiments quantifying the benefit of our improvements. For unconditional image synthesis on CIFAR-10 and CelebA, our modifications yield the best known FID scores on various GAN architectures. For conditional image synthesis on CIFAR-10, we improve the state-of-the-art FID score from 11.48 to 9.21. Finally, on ImageNet-2012, we apply our technique to the original BigGAN model and improve the FID from 6.66 to 5.38, which is the best score at that model size.
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Cited by 1 Pith paper
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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.
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