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A fast & flexible implementation of Variational Autoencoders using PyTorch

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VAE

This is a generic and fast implementation of variational auto-encoders in SP float32 (GTX and RTX series Nvidia GPUs
have significantly higher SP performance vs DP) in PyTorch.

For a different usage (dataset), just a new constructs file needs to be written.


An example usage on the MNIST dataset is provided.

2-D latent space:

49D latent space:

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A fast & flexible implementation of Variational Autoencoders using PyTorch

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