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Documentation

pythae

This library implements some of the most common (Variational) Autoencoder models under a unified implementation. In particular, it provides the possibility to perform benchmark experiments and comparisons by training the models with the same autoencoding neural network architecture. The feature make your own autoencoder allows you to train any of these models with your own data and own Encoder and Decoder neural networks. It integrates experiment monitoring tools such wandb, mlflow or comet-ml 🧪 and allows model sharing and loading from the HuggingFace Hub 🤗 in a few lines of code.

News 📢

As of v0.1.0, Pythae now supports distributed training using PyTorch's DDP. You can now train your favorite VAE faster and on larger datasets, still with a few lines of code. See our speed-up benchmark.

Quick access:

• Installation
• Implemented models / Implemented samplers
• Reproducibility statement / Results flavor
• Model training / Data generation / Custom network architectures / Distributed training
• Model sharing with 🤗 Hub / Experiment tracking with wandb / Experiment tracking with mlflow / Experiment tracking with comet_ml
• Tutorials / Documentation
• Contributing 🚀 / Issues 🛠️
• Citing this repository

Installation

To install the latest stable release of this library run the following using pip

$ pip install pythae

To install the latest github version of this library run the following using pip

$ pip install git+https://github.com/clementchadebec/benchmark_VAE.git

or alternatively you can clone the github repo to access to tests, tutorials and scripts.

$ git clone https://github.com/clementchadebec/benchmark_VAE.git

and install the library

$ cd benchmark_VAE
$ pip install -e .

Available Models

Below is the list of the models currently implemented in the library.

Models	Training example	Paper	Official Implementation
Autoencoder (AE)
Variational Autoencoder (VAE)		link
Beta Variational Autoencoder (BetaVAE)		link
VAE with Linear Normalizing Flows (VAE_LinNF)		link
VAE with Inverse Autoregressive Flows (VAE_IAF)		link	link
Disentangled Beta Variational Autoencoder (DisentangledBetaVAE)		link
Disentangling by Factorising (FactorVAE)		link
Beta-TC-VAE (BetaTCVAE)		link	link
Importance Weighted Autoencoder (IWAE)		link	link
Multiply Importance Weighted Autoencoder (MIWAE)		link
Partially Importance Weighted Autoencoder (PIWAE)		link
Combination Importance Weighted Autoencoder (CIWAE)		link
VAE with perceptual metric similarity (MSSSIM_VAE)		link
Wasserstein Autoencoder (WAE)		link	link
Info Variational Autoencoder (INFOVAE_MMD)		link
VAMP Autoencoder (VAMP)		link	link
Hyperspherical VAE (SVAE)		link	link
Poincaré Disk VAE (PoincareVAE)		link	link
Adversarial Autoencoder (Adversarial_AE)		link
Variational Autoencoder GAN (VAEGAN) 🥗		link	link
Vector Quantized VAE (VQVAE)		link	link
Hamiltonian VAE (HVAE)		link	link
Regularized AE with L2 decoder param (RAE_L2)		link	link
Regularized AE with gradient penalty (RAE_GP)		link	link
Riemannian Hamiltonian VAE (RHVAE)		link	link

See reconstruction and generation results for all aforementionned models

Available Samplers

Below is the list of the models currently implemented in the library.

| Samplers | Models | Paper | Official Implementation | |:-------------------------------------:|:-------------------:|:-------