There are 8 repositories under variational-autoencoders topic.
A Collection of Variational Autoencoders (VAE) in PyTorch.
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
Bayesian Deep Learning: A Survey
Easy generative modeling in PyTorch.
DGMs for NLP. A roadmap.
I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
Repository for Deep Structural Causal Models for Tractable Counterfactual Inference
Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling
Voxel-Based Variational Autoencoders, VAE GUI, and Convnets for Classification
Official implementation of Dynamical VAEs
Deep and Machine Learning for Microscopy
Ladder Variational Autoencoders (LVAE) in PyTorch
This repository tries to provide unsupervised deep learning models with Pytorch
Code for the paper "Improving Variational Auto-Encoders using Householder Flow" (https://arxiv.org/abs/1611.09630)
Deep active inference agents using Monte-Carlo methods
Computer code collated for use with Artificial Intelligence Engines book by JV Stone
Code for the Paper: "Conditional Variational Capsule Network for Open Set Recognition", Y. Guo, G. Camporese, W. Yang, A. Sperduti, L. Ballan, ICCV, 2021.
An experimental deep learning & genotype network-based system for predicting new influenza protein sequences.
Transfer learning for flight-delay prediction via variational autoencoders in Keras
There are C language computer programs about the simulator, transformation, and test statistic of continuous Bernoulli distribution. More than that, the book contains continuous Binomial distribution and continuous Trinomial distribution.
implement Deep Feature Consisten Variational Autoencoder in Tensorflow
Orgainzed Digital Intelligent Network (O.D.I.N)
PyTorch re-implementation of [Structured Inference Networks for Nonlinear State Space Models, AAAI 17]
An IPython notebook explaining the concepts of Variational Autoencoders and building one using Keras to generate new faces.
Unsupervised speech enhancement using DVAEs
Code for "SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation" @ ICML 2022
A Keras/TensorFlow-based implementation of Adversarial Variational Bayes by L. Mescheder et al.
Use auto encoder feature extraction to facilitate classification model prediction accuracy using gradient boosting models
Recurrent neural network in TensorFlow for generating novel monophonic melodies.