IParraMartin/Sparse-Autoencoder
A PyTorch implementation of a Sparse Auto Encoder (SAE) using MSE loss and KL Divergence penalty
This project provides an easy-to-understand implementation of a sparse autoencoder, a type of neural network that learns to represent data efficiently. It takes in raw data, like images, and learns a compressed, meaningful representation. This tool is designed for students or researchers who want to learn how these models work under the hood.
No commits in the last 6 months.
Use this if you are a student or researcher looking for an educational, from-scratch implementation of a sparse autoencoder to understand its core mechanics.
Not ideal if you need a high-performance or production-ready solution for data compression or feature extraction.
Stars
27
Forks
1
Language
Python
License
—
Last pushed
Sep 26, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/IParraMartin/Sparse-Autoencoder"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Naresh1318/Adversarial_Autoencoder
A wizard's guide to Adversarial Autoencoders
mseitzer/pytorch-fid
Compute FID scores with PyTorch.
acids-ircam/RAVE
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
ratschlab/aestetik
AESTETIK: Convolutional autoencoder for learning spot representations from spatial...
jaanli/variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)