jaanli/variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
This project offers foundational code for a Variational Autoencoder (VAE), a machine learning model that learns to represent complex data efficiently. You input datasets like images (e.g., handwritten digits), and it outputs a compressed, meaningful representation of that data, as well as the ability to generate new, similar data. It's designed for machine learning researchers and practitioners exploring generative models and data compression.
1,183 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or student who needs a clear, well-tested reference implementation of a Variational Autoencoder to understand or build upon generative models.
Not ideal if you're looking for an out-of-the-box solution to integrate into a production application without deep understanding or modification, as this is a research-focused reference.
Stars
1,183
Forks
258
Language
Python
License
MIT
Last pushed
Apr 24, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/jaanli/variational-autoencoder"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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...
ethanluoyc/pytorch-vae
A Variational Autoencoder (VAE) implemented in PyTorch