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VAE

A variational autoencoder (VAE) is a generative model that combines deep learning with Bayesian inference to learn compact latent representations of data. VAEs are widely used for image generation, anomaly detection, and data augmentation.

Here are 6 public repositories matching this topic...

koba-jon / pytorch_cpp

Deep Learning sample programs using PyTorch in C++

Updated Jan 22, 2026
C++

navneet-nmk / Pytorch-RL-CPP

A Repository with C++ implementations of Reinforcement Learning Algorithms (Pytorch)

Updated Jul 18, 2019
C++

meiyulee / continuous_Bernoulli

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.

c deep-neural-networks simulator deep-learning cpp statistical-analysis vae deeplearning autoencoders test-statistic variational-autoencoder variational-autoencoders deeplearning-ai vae-implementation continuous-bernoulli point-estimator

Updated May 6, 2024
C++

curieuxjy / go2_dreamwaq

Go2 quadruped locomotion with unofficial DreamWaQ implementation for rough terrain walking

vae ppo quadruped-robot isaacgym dreamwaq

Updated Dec 3, 2025
C++

aaronbae / ModifiedSentenceVAE

NLP Modeling for Paraphrase Generation

nlp gan vae vae-gan paraphrase paraphrase-generation

Updated Jun 22, 2022
C++

mimir-framework

brit45 / mimir-framework

CPU-first deep learning framework in C++17 with Lua scripting, model registry, SafeTensors serialization, and native support for VAE, diffusion, and transformer architectures.

machine-learning cpu lua deep-learning cpp vulkan opencl openmp vae diffusion safetensors

Updated Apr 1, 2026
C++

Followers: 10 followers
Website: github.com/topics/vae
Wikipedia: Wikipedia