Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes

@article{He2018DiverseCI,
  title={Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes},
  author={Yang He and Bernt Schiele and Mario Fritz},
  journal={CoRR},
  year={2018},
  volume={abs/1808.01121}
}
Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images. On the one hand, Generative Adversarial Networks (GANs) have contributed a highly effective adversarial learning procedure, but still suffer from stability issues. On the other hand, Conditional Variational Auto-Encoders (CVAE) models provide a sound way of conditional modeling but suffer from mode-mixing issues. Therefore, recent work has turned back to simple and stable… CONTINUE READING
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