adeshpande3/Generative-Adversarial-Networks
Tutorial on GANs
This tutorial helps deep learning researchers and practitioners understand Generative Adversarial Networks (GANs) and how they function. It explains how two neural networks—a generator and a discriminator—learn to create realistic new images by competing against each other. The input is an existing dataset of images, and the output is new, natural-looking images that are indistinguishable from the original data.
293 stars. No commits in the last 6 months.
Use this if you are a deep learning practitioner looking for a practical, code-based introduction to Generative Adversarial Networks using Python and TensorFlow.
Not ideal if you are looking for a plug-and-play solution to generate images without understanding the underlying deep learning concepts.
Stars
293
Forks
126
Language
Jupyter Notebook
License
—
Category
Last pushed
Aug 06, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/adeshpande3/Generative-Adversarial-Networks"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
huggingface/pytorch-pretrained-BigGAN
🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
torchgan/torchgan
Research Framework for easy and efficient training of GANs based on Pytorch
metal3d/keras-video-generators
Keras generators to generate sequences from videos as input
GANs-in-Action/gans-in-action
Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks
junyanz/pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch