Training Wasserstein Generative Adversarial Networks Without Gradient Penalties

Training Wasserstein Generative Adversarial Networks Without Gradient Penalties

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=H3k84EaivhM


Duration: 39:55
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Dohyun Kwon (University of Wisconsin, Madison)
Training Wasserstein Generative Adversarial Networks Without Gradient Penalties
Dynamics and Discretization: PDEs, Sampling, and Optimization



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Dynamics and Discretization: PDEs Sampling and Optimization
Dohyun Kwon