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AntixK / PyTorch-VAE

A Collection of Variational Autoencoders (VAE) in PyTorch.
Apache License 2.0
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VAE with variational nested dropout #60

Open ralphc1212 opened 2 years ago

ralphc1212 commented 2 years ago

Hi Authors,

We ask to contribute our new VAE with variational nested dropout (VNDAE) presented in our recent preprint https://arxiv.org/pdf/2101.11353.pdf. It was shown to outperform VAE, BetaVAE, JointVAE and SWAE evaluated under FID and IS, on CelebA, Cifar10, Cifar100, 3D Chairs and Chest X-ray.

Please check our pull request. Thanks.

Best, Ralph CUI

HareshKarnan commented 2 years ago

Hi, the input image is normalized between 0-1, did you consider using a nn.Sigmoid() activation layer instead of nn.Tanh() as the output activation of your decoder?