akanazawa / vgan Goto Github PK
View Code? Open in Web Editor NEWCode for image generation of Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
License: MIT License
Code for image generation of Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
License: MIT License
As titled.
Hi @akanazawa , it would be great if you could release the pretrained models of CelebA-HQ or other models in your paper. Do you have any plans to release the models?
Hi,
Thank you for releasing nice paper and code.
Refer to Eq (11), I think the generator G should be trained using mean outputs of the encoder E. However, as can be seen here and here, G is trained using the reparameterization outputs of E rathen mean.
Is this intended? Any explanation for the inconsistency between the code and equation?
Thanks,
Yunjey
Hi,
Could you please tell me what image format you chosed?
Thank you very much!
Hi,
Can you explain how you compute
hi, @akanazawa How to explain the combination of standard gan and gradient penalty when you run the experiments on the celeba dataset.
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