pytorch-vae and VAE-CVAE-MNIST

pytorch-vae

Established

VAE-CVAE-MNIST

Emerging

Maintenance 0/25

Adoption 10/25

Maturity 16/25

Community 24/25

Maintenance 2/25

Adoption 10/25

Maturity 8/25

Community 23/25

Stars: 432

Forks: 107

Downloads: —

Commits (30d): 0

Language: Python

License: BSD-3-Clause

Stars: 658

Forks: 112

Downloads: —

Commits (30d): 0

Language: Python

License: —

Stale 6m No Package No Dependents

No License Stale 6m No Package No Dependents

About pytorch-vae

ethanluoyc/pytorch-vae

A Variational Autoencoder (VAE) implemented in PyTorch

This is a foundational building block for machine learning engineers and researchers working with deep learning models. It takes in complex data, like images or text, and learns a compressed, meaningful representation of that data. This compressed representation can then be used for generating new, similar data, or for tasks like anomaly detection.

deep-learning generative-modeling data-compression representation-learning anomaly-detection

About VAE-CVAE-MNIST

timbmg/VAE-CVAE-MNIST

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

This project helps machine learning engineers and researchers understand how Variational Autoencoders (VAEs) and Conditional VAEs (CVAEs) learn to generate images. It takes a dataset of handwritten digit images as input and outputs newly generated, realistic-looking digits, demonstrating the model's ability to learn and reconstruct visual patterns. This is ideal for those exploring generative models.

generative-modeling image-synthesis deep-learning-research machine-learning-education

Related comparisons

pytorch-vae and variational-autoencoder pytorch-vae and PyTorch-VAE pytorch-vae and pytorch-vq-vae pytorch-vae and s-vae-pytorch

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