Least Squares Generative Adversarial Networks

@article{Mao2017LeastSG,
  title={Least Squares Generative Adversarial Networks},
  author={Xudong Mao and Qing Li and Haoran Xie and Raymond Y. K. Lau and Zhen Wang and Stephen Paul Smolley},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2813-2821}
}
Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We… CONTINUE READING

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