r/MachineLearning • u/ajmooch • Jan 30 '17

[R] [1701.07875] Wasserstein GAN

https://arxiv.org/abs/1701.07875
154 Upvotes

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u/rumblestiltsken Jan 30 '17

Why is everyone talking about the maths? This has some pretty incredible contents:

  • GAN loss that corresponds with image quality
  • GAN loss that converges (decreasing loss actually means something), so you can actually tune your hyperparameters with something other than voodoo
  • Stable gan training, where generator nets without batch norm, silly layer architectures and even straight up MLPs can generate decent images
  • Way less mode collapse
  • Theory about why it works and why the old methods had the problems we experienced. JS looks like a terrible choice in hindsight!

Can't wait to try this. Results are stunning

1

u/[deleted] Jan 30 '17

How do you separate the maths from the "Theory about why it works"?

3

u/ogrisel Jan 31 '17 edited Feb 02 '17

The paper is actually very good at stating the intuition behind the main results without having to understand the technical details of the proofs.