This repository is a Torch version of Building Autoencoders in Keras, but only containing code for reference - please refer to the original blog post for an explanation of autoencoders. Training hyperparameters have not been adjusted. The following models are implemented:

• AE: Fully-connected autoencoder
• SparseAE: Sparse autoencoder
• DeepAE: Deep (fully-connected) autoencoder
• ConvAE: Convolutional autoencoder
• UpconvAE: Upconvolutional autoencoder - also known by several other names (bonus)
• DenoisingAE: Denoising (convolutional) autoencoder
• Seq2SeqAE: Sequence-to-sequence autoencoder
• VAE: Variational autoencoder
• AdvAE: Adversarial autoencoder (bonus)

Different models can be chosen using th main.lua -model <modelName>.

The following luarocks packages are required:

• mnist
• dpnn (for DenoisingAE, VAE)
• rnn (for Seq2SeqAE)