Autoencoders

This is the heart of pythae! Here are implemented some of the most common (Variational) Autoencoders models.

By convention, each implemented model is stored in a folder located in pythae.models and named likewise the model. The following modules can be found in this folder:

• modelname_config.py: Contains a ModelNameConfig instance inheriting from either AEConfig for Autoencoder models or VAEConfig for Variational Autoencoder models.
• modelname_model.py: An implementation of the model inheriting either from AE for Autoencoder models or VAE for Variational Autoencoder models.

• modelname_utils.py (optional): A module where utils methods are stored.

Available Models

BaseAE	Base class for Autoencoder based models.
AutoModel	Utils class allowing to reload any pythae.models automatically
AE	Vanilla Autoencoder model.
VAE	Vanilla Variational Autoencoder model.
BetaVAE	\(\beta\)-VAE model.
VAE_LinNF	Variational Auto Encoder with linear Normalizing Flows model.
VAE_IAF	Variational Auto Encoder with Inverse Autoregressive Flows (IAF).
DisentangledBetaVAE	Disentangled \(\beta\)-VAE model.
FactorVAE	FactorVAE model.
BetaTCVAE	\(\beta\)-TCVAE model.
IWAE	Importance Weighted Autoencoder model.
CIWAE	Combination Importance Weighted Autoencoder model.
MIWAE	Multiply Importance Weighted Autoencoder model.
PIWAE	Partially Importance Weighted Autoencoder model.
MSSSIM_VAE	VAE using perseptual similarity metrics model.
WAE_MMD	Wasserstein Autoencoder model.
INFOVAE_MMD	Info Variational Autoencoder model.
VAMP	Variational Mixture of Posteriors (VAMP) VAE model
SVAE	\(\mathcal{S}\)-VAE model.
PoincareVAE	Poincaré Variational Autoencoder model.
Adversarial_AE	Adversarial Autoencoder model.
VAEGAN	Variational Autoencoder using Adversarial reconstruction loss model.
VQVAE	Vector Quantized-VAE model.
HVAE	Hamiltonian VAE.
RAE_GP	Regularized Autoencoder with gradient penalty model.
RAE_L2	Regularized Autoencoder with L2 decoder params regularization model.
RHVAE	Riemannian Hamiltonian VAE model.