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GANs in Action by Jakub Langr, Vladimir Bok

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Chapter 5. Training and common challenges: GANing for success

This chapter covers

  • Meeting the challenges of evaluating GANs
  • Min-Max, Non-Saturating, and Wasserstein GANs
  • Using tips and tricks to best train a GAN
Note

When reading this chapter, please remember that GANs are notoriously hard to both train and evaluate. As with any other cutting-edge field, opinions about what is the best approach are always evolving.

Papers such as “How to Train Your DRAGAN” are a testament to both the incredible capacity of machine learning researchers to make bad jokes and the difficulty of training Generative Adversarial Networks well. Dozens of arXiv papers preoccupy themselves solely with the aim of improving the training of GANs, ...

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