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Autoencoders

PyTorch implementation of various autoencoder architectures using the MNIST dataset.
Results are in the jupyter notebooks

Architectures implemented :

  1. Vanilla autoencoder (using FCN)
  2. Denoising autoencoder (using FCN)(using convolutions)

TO-DO :

  • Sparse autoencoder
  • Variational autoencoder
  • Beta-VAE

Resources

  1. From Autoencoder to beta-VAE (including research papers cited in the blog)