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Penalty Gradient Normalization for Generative Adversarial Networks

arXiv 2306.13576 ↗First posted 23 Jun 2023cs.CV cs.LGcited by 0

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Abstract (arXiv)

In this paper, we propose a novel normalization method called penalty gradient normalization (PGN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the sharp gradient space. Unlike existing work such as gradient penalty and spectral normalization, the proposed PGN only imposes a penalty gradient norm constraint on the discriminator function, which increases the capacity of the discriminator. Moreover, the proposed penalty gradient normalization can be applied to different GAN architectures with little modification. Extensive experiments on three datasets show that GANs trained with penalty gradient normalization outperform existing methods in terms of both Frechet Inception and Distance and Inception Score.

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References · 26

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14 more
  1. 2017Generalization and Equilibrium in Generative Adversarial Nets (GANs)cited by 166
  2. 2017The Shattered Gradients Problem: If resnets are the answer, then what is the question?cited by 157
  3. 2019Consistency Regularization for Generative Adversarial Networkscited by 136
  4. 2016Neural Photo Editing with Introspective Adversarial Networkscited by 109
  5. 2019AutoGAN: Neural Architecture Search for Generative Adversarial Networkscited by 94
  6. 2015How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?cited by 78
  7. 2020Improved Consistency Regularization for GANscited by 69
  8. 2019Adversarial Lipschitz Regularizationcited by 30
  9. 2018A Large-Scale Study on Regularization and Normalization in GANscited by 26
  10. 2020Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Searchcited by 25
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  12. 2019Lipschitz Generative Adversarial Netscited by 24
  13. 2020LT-GAN: Self-Supervised GAN with Latent Transformation Detectioncited by 11
  14. 2020Adaptive Weighted Discriminator for Training Generative Adversarial Networkscited by 3

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