metadata
tags:
- huggan
- gan
license: mit
Generate fauvism still life image using FastGAN
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.
This model was trained on a dataset of 64 high-quality images of Shells.
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
Generated Images
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}
}