Assisting the Adversary to Improve GAN Training

  title={Assisting the Adversary to Improve GAN Training},
  author={Andreas Munk and William Harvey and Frank D. Wood},
  journal={2021 International Joint Conference on Neural Networks (IJCNN)},
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely overlooked regularization technique which we refer to as the Adversary's Assistant (AdvAs). We motivate this using a different perspective to that of prior work. Specifically, we consider a common mismatch between theoretical analysis and practice: analysis often assumes that the discriminator reaches its optimum on each… 

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