Disentangled Variational Auto-Encoder in TensorFlow / Keras (Beta-VAE)

Joint variational Autoencoders for Multimodal Imputation and Embedding (JAMIE)

Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder

State arrays for single particle tracking

Denoising Variational Autoencoder

automatic/analytical differentiation benchmark

TensorFlow implementation of the method from Variational Dropout Sparsifies Deep Neural Networks, Molchanov et al. (2017)

[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".

Python toolbox for solving imaging continuous optimization problems.

Discrete Variational Autoencoder in PyTorch

Code for Adversarial Approximate Inference for Speech to Laryngograph Conversion

Disentangling the latent space of a VAE.

Experiments on Disentangled Representation Learning using Variational autoencoding algorithms

Variational Diagrammatic Monte-Carlo Built on Dynamical Mean-Field Theory

A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.