variational-autoencoder

Collection of generative models in Tensorflow

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Notebooks about Bayesian methods for machine learning

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

Deep probabilistic analysis of single-cell and spatial omics data

"Deep Generative Modeling": Introductory Examples

My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

Next RecSys Library

PyTorch implementation of normalizing flow models

Experiments for understanding disentanglement in VAE latent representations

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

Variational autoencoders for collaborative filtering

We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.

Tensorflow implementation of variational auto-encoder for MNIST

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

Open-AI's DALL-E for large scale training in mesh-tensorflow.