A Collection of Variational Autoencoders (VAE) in PyTorch.

Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)

Code for "Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection", ECCV 2022

Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control", ICASSP 2022

Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"

Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words

TensorFlow GAN implementation using Gumbel Softmax

An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.

Keras implementation of a Variational Auto Encoder with a Concrete Latent Distribution

TensorFlow-based implementation of "Attend, Infer, Repeat" paper (Eslami et al., 2016, arXiv:1603.08575).

A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation

Code for TACL 2022 paper on Data-to-text Generation with Variational Sequential Planning

Implementation of the Gumbel-Sigmoid distribution in PyTorch.

Official project of DiverseSampling (ACMMM2022 Paper)

GAN-Based Text Generation

Black-box spike and slab variational inference, example with linear models

Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder

Python library for the differentiable hypergeometric distribution

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

The implementation of Gumbel softmax reparametrization trick for discrete VAE