PyTorch VAE
A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility.
The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. All the models are trained on the CelebA dataset for consistency and comparison.
The architecture of all the models are kept as similar as possible with the same layers, except for cases where the original paper necessitates a radically different architecture (Ex. VQ VAE uses Residual layers and no Batch-Norm, unlike other models). Here are the results of each model.
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