A Collection of Variational Autoencoders (VAE) in PyTorch.

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

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.

Tensorflow 2.x implementation of the beta-TCVAE (arXiv:1802.04942).

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

An official repository for a VAE tutorial of Probabilistic Modelling and Reasoning (2023/2024) - a University of Edinburgh master's course.

Python implementation of N-gram Models, Log linear and Neural Linear Models, Back-propagation and Self-Attention, HMM, PCFG, CRF, EM, VAE

Variational Auto Encoders (VAEs), Generative Adversarial Networks (GANs) and Generative Normalizing Flows (NFs) and are the most famous and powerful deep generative models.

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

Implementation of the variational autoencoder with PyTorch and Fastai

A re-implementation of the Sentence VAE paper, Generating Sentences from a Continuous Space

Towards Generative Modeling from (variational) Autoencoder to DCGAN

Autoencoders (Standard, Convolutional, Variational), implemented in tensorflow

VAE and CVAE pytorch implement based on MNIST

Testing the Reproducibility of the paper: MixSeq. Under the assumption that macroscopic time series follow a mixture distribution, they hypothesise that lower variance of constituting latent mixture components could improve the estimation of macroscopic time series.

Pytorch版本实现的VAE和公式推导

Running VAEs on mobile and IOT devices using TFLite.

ColorVAE is a Vanilla Auto Encoder (V.A.E.) which can be used to add colours to black and white images.

Utilized a VAE (Variational Autoencoder) and CGAN (Conditional Generative Adversarial Network) models to generate synthetic chatter signals, addressing the challenge of imbalanced data in turning operations. Compared othe performance of synthetic chatter signals.