metadata
tags:
- huggan
- gan
license: mit
fastgan-few-shot-fauvism-still-life
Model description
FastGAN model is a Generative Adversarial Networks (GAN) training on a small amount of high-fidelity images with minimum computing cost. Using a skip-layer channel-wise excitation module and a self-supervised discriminator trained as a feature-encoder, the model was able to converge after some hours of training for either 100 high-quality images or 1000 images datasets.
How to use
# You can include sample code which will be formatted
Limitations and bias
- Converge faster and better with small datasets (less than 1000 samples)
Training data
Training procedure
Preprocessing, hardware used, hyperparameters...
Eval results
Generated Images
You can embed local or remote images using 
BibTeX entry and citation info
@article{FastGAN,
title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis},
author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal},
journal={ICLR},
year={2021}
}