matthewvowels1/Awesome-VAEs
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
This is a comprehensive, curated list of research papers focused on Variational Autoencoders (VAEs), disentanglement, and related generative models. It helps researchers and PhD students quickly find relevant literature in machine learning for tasks like generating electronic health records, analyzing gravitational waveforms, or improving image processing. The resource organizes hundreds of papers by year, providing direct links to the publications.
842 stars. No commits in the last 6 months.
Use this if you are a researcher or student working with VAEs, generative models, or representation learning and need a centralized, organized collection of academic papers.
Not ideal if you are looking for ready-to-use code, tutorials, or a high-level overview of VAEs without diving into academic literature.
Stars
842
Forks
74
Language
—
License
—
Last pushed
Jul 11, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/matthewvowels1/Awesome-VAEs"
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)