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variational-autoencoder

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 3 public repositories matching this topic...

mahindrautela / VAE-compositesdiscovery

Variational Autoencoders for composites generation

composite-materials variational-autoencoder guided-waves material-discovery

Updated Sep 27, 2024
PureBasic

Ray16 / ALCF_AI_Hackathon_Team_Borides

This repo contains code used to tackle Challenge I of AI Hackathon

variational-autoencoder 1d-cnn

Updated Dec 1, 2022
PureBasic

Sudhanshu1304 / Machine-Learns

An Interactive Website to do hands-on Machine learning. An online platform for learning and visualizing machine learning and deep learning concepts. Here you can see algorithm in action. Examples: Read about Autoencoder and see how they work.

website machine-learning django deep-learning tensorflow generative-model variational-autoencoder

Updated Dec 14, 2024
PureBasic

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