PacGAN: The power of two samples in generative adversarial networks

@article{Lin2017PacGANTP,
  title={PacGAN: The power of two samples in generative adversarial networks},
  author={Zinan Lin and Ashish Khetan and Giulia C. Fanti and Sewoong Oh},
  journal={CoRR},
  year={2017},
  volume={abs/1712.04086}
}
Generative adversarial networks (GANs) are innovative techniques for learning generative models of complex data distributions from samples. Despite remarkable recent improvements in generating realistic images, one of their major shortcomings is the fact that in practice, they tend to produce samples with little diversity, even when trained on diverse datasets. This phenomenon, known as mode collapse, has been the main focus of several recent advances in GANs. Yet there is little understanding… CONTINUE READING
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