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
- CatVAE: Categorical variational autoencoder (bonus)
- AAE: Adversarial autoencoder (bonus)
Different models can be chosen using th main.lua -model <modelName>.
MCMC sampling can be used for VAEs, CatVAEs and AAEs with th main.lua -model <modelName> -mcmc <steps>. To see the effects of MCMC sampling with this simple setup it is best to choose a large standard deviation, e.g. -sampleStd 5, for the Gaussian distribution to draw the initial samples from.
The following luarocks packages are required:
- mnist
- dpnn (for DenoisingAE)
- rnn (for Seq2SeqAE)