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Attend our talk tomorrow at #ICML2020 on Implicit competitive regularization in GANs. @Kay12400259 @Caltech
Paper: https://arxiv.org/abs/1910.05852
Blog: https://f-t-s.github.io/projects/icr/
Poster: https://icml.cc/virtual/2020/poster/6721 …
We show that GAN performance can not arise from divergence minimization but relies on "implicit competitive regularization" due to simultaneous training. This prevents discriminator from getting too perfect, leading to pathological points.pic.twitter.com/s0E3wDPDSv
Implicit competitive regularization in GANs encourages discriminators to learn slowly. It is more effective than explicit gradient penalties. Competitive gradient descent further strengthens this implicit reg. by using mixed Hessian to account for interaction among playerspic.twitter.com/KewCkGd24X
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