Training Wasserstein Generative Adversarial Networks Without Gradient Penalties
berkeley.edu
Training Wasserstein Generative Adversarial Networks Without Gradient Penalties
Generative Adversarial Networks (GANs) have seen remarkable success in the field of machine learning. In the Wasserstein GAN framework, the core idea is to solve the minimax problem over the generator, and the discriminator which is restricted to be a 1-Lipschitz function. In this talk, we discuss how to enforce a 1-Lipschitz bound without the ...
Oct 25, 2021
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