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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 5 public repositories matching this topic...

matheus695p / fraud-detection

Fraud detection

nn xgboost vae logistic-regression convolutional-neural-networks gans svm-classifier k-nearest-neighbor-classifier tabgan

Updated Jun 13, 2021
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jayin92 / TerrainGAN

TerrainGAN: a combination of VAE and GAN that can generate realistic 3D terrain

terrain-generation vae pix2pix

Updated Dec 9, 2023
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carlosholivan / timbre-vae

A VAE trained with CQTs of isolated pitches from recordings of the London Philarmonic Orchestra

music cnn vae pitch

Updated Dec 19, 2020
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orroo / DEEP-NEPHRO

DEEP_NEPHRO is a university-led project focused on enhancing dialysis care through intelligent monitoring and optimization. By integrating machine learning and real-time data analysis, the project aims

python machine-learning monitoring deep-learning machine-learning-algorithms patient university-project prediction cnn lstm autoencoder vae academic-project patient-monitoring real-time-analysis nephrology dialysis healthcare-ai medical-optimization

Updated May 21, 2025
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falaktheoptimist / vae

Tutorial for VAE

vae

Updated Dec 27, 2019
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Followers: 10 followers
Website: github.com/topics/vae
Wikipedia: Wikipedia