1Konny/VQ-VAE
Pytorch Implementation of "Neural Discrete Representation Learning"
This project helps machine learning researchers and practitioners explore discrete latent representations for image data. It takes raw image datasets like MNIST or CIFAR10 as input and outputs reconstructed images, demonstrating how well the model learns to compress and represent visual information. It is designed for those experimenting with advanced generative models.
No commits in the last 6 months.
Use this if you are a machine learning researcher or student interested in understanding and applying Vector Quantized Variational Autoencoders (VQ-VAE) for image generation and representation learning.
Not ideal if you need a pre-trained, production-ready model for immediate use in an application or if you are not comfortable working with Python and PyTorch code.
Stars
92
Forks
12
Language
Jupyter Notebook
License
—
Last pushed
Mar 23, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/1Konny/VQ-VAE"
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)