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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...

googlecreativelab / beat-blender

Blend beats using machine learning to create music in a fun new way.

javascript music machine-learning tensorflow vae deeplearnjs

Updated Mar 27, 2018
JavaScript

mobeets / neural-synth

an interface for generating music with a VAE

music neural-network vae music-generation neural-art variational-autoencoder neural-music

Updated Aug 28, 2022
JavaScript

greentec / VAE_OnlineDemo_Dungeon2D

generate dungeon shape using VAE.

vae dungeon-shape

Updated Apr 25, 2017
JavaScript

ToNyOyO / veeva-approved-email-package-manager

Veeva Vault Approved Email package manager

fragments vault vae veeva veeva-approved-email

Updated Aug 21, 2020
JavaScript

inventshah / CMSC838C-Project

Data driven BRDF Material Editing for CMSC838C

rendering vae brdf

Updated May 18, 2023
JavaScript

cnatthaphon / surrogate-studio

A schema-driven ML experimentation platform — visual graph editor, 23 models across GANs, diffusion, VAEs, and physics surrogates. Runs in browser (TF.js) or PyTorch server.

machine-learning deep-learning neural-network gan vae visual-programming diffusion tensorflow-js

Updated Apr 10, 2026
JavaScript

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Website: github.com/topics/vae
Wikipedia: Wikipedia