csinva/gan-vae-pretrained-pytorch
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
This project provides pre-trained models for generating new images and classifying existing ones, specifically for common datasets like MNIST and CIFAR. It takes numerical image data as input and can either produce novel, synthetic images resembling the original dataset or classify input images into categories. This is useful for researchers and machine learning practitioners who need to quickly experiment with generative models or image classifiers without extensive training from scratch.
207 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or practitioner working with image generation or classification and need pre-trained models for common datasets like MNIST or CIFAR to jumpstart your experiments or comparisons.
Not ideal if you need to generate highly complex, realistic images beyond simple datasets or require models trained on custom, domain-specific image data.
Stars
207
Forks
49
Language
Jupyter Notebook
License
—
Last pushed
Feb 02, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/csinva/gan-vae-pretrained-pytorch"
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)