---
tags:
- huggan
- gan
# See a list of available tags here:
# https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts#L12
# task: unconditional-image-generation or conditional-image-generation or image-to-image
license: mit
---

# fastgan-few-shot-fauvism-still-life

## Model description

[FastGAN model](https://arxiv.org/abs/2101.04775) 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

```python
# 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

[few-shot-fauvism-still-life](https://huggingface.co/datasets/huggan/few-shot-fauvism-still-life)

## Training procedure

Preprocessing, hardware used, hyperparameters...

## Eval results

## Generated Images

You can embed local or remote images using `![](...)`

### BibTeX entry and citation info

```bibtex
@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}
}
```