vae-implementation

A Collection of Variational Autoencoders (VAE) in PyTorch.

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

Implementation of mutual learning model between VAE and GMM.

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.

Dirichlet-Variational Auto-Encoder by PyTorch

Pytorch implementation of Gaussian Mixture Variational Autoencoder GMVAE

Implementation of LiteVAE

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

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

Learn Machine learning by doing exercises and intuitive animations

Symbol emergence using Variational Auto-Encoder and Gaussian Mixture Model (Inter-GMM-VAE)~VAEを活用した実画像からの記号創発~

Topics include function approximation, learning dynamics, using learned dynamics in control and planning, handling uncertainty in learned models, learning from demonstration, and model-based and model-free reinforcement learning.

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

Pytorch版本实现的VAE和公式推导

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

Optimized PyTorch/Triton MiniMax H3 video VAE for ComfyUI, with reproducible benchmarks and optional mixed INT8 acceleration—no TensorRT engine required.

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