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Feature Statistics Mixing Regularization for Generative Adversarial Networks
[article]
2022
arXiv
pre-print
In generative adversarial networks, improving discriminators is one of the key components for generation performance. As image classifiers are biased toward texture and debiasing improves accuracy, we investigate 1) if the discriminators are biased, and 2) if debiasing the discriminators will improve generation performance. Indeed, we find empirical evidence that the discriminators are sensitive to the style (e.g., texture and color) of images. As a remedy, we propose feature statistics mixing
arXiv:2112.04120v2
fatcat:4s6ty4ui4nag7ksybvmcyxq7ri