LOGAN: Latent Optimisation for Generative Adversarial Networks

Wu Yan
Wu Yan
Lillicrap Timothy
Lillicrap Timothy
Cited by: 23|Views155

Abstract:

Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes. In this work, we introduce a new form of latent optimisation inspired by the CS-GAN and show that it improves adversarial dynamics by enhancing in...More

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