Wasserstein Generative Adversarial Networks (WGAN & WGAN-GP) in PyTorch

Wasserstein Generative Adversarial Networks (WGAN & WGAN-GP) in PyTorch

Wasserstein Generative Adversarial Networks (WGAN & WGAN-GP) in PyTorch. We implement WGAN and WGAN-GP in PyTorch. Both of these improvements are based on the loss function of GANs and focused specifically on improving the stability of training.

In this video we implement WGAN and WGAN-GP in PyTorch. Both of these improvements are based on the loss function of GANs and focused specifically on improving the stability of training.

Github Repository: https://github.com/aladdinpersson/Machine-Learning-Collection

OUTLINE:

  • 0:00 - Introduction
  • 0:27 - Understanding WGAN
  • 6:53 - WGAN Implementation details
  • 9:15 - Coding WGAN
  • 15:50 - Understanding WGAN-GP
  • 18:48 - Coding WGAN-GP
  • 25:29 - Ending

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