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--- |
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tags: |
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- huggan |
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- gan |
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license: mit |
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--- |
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# fastgan-few-shot-fauvism-still-life |
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## Model description |
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[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. |
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#### How to use |
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```python |
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# You can include sample code which will be formatted |
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``` |
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#### Limitations and bias |
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* Converge faster and better with small datasets (less than 1000 samples) |
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## Training data |
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[few-shot-fauvism-still-life](https://huggingface.co/datasets/huggan/few-shot-fauvism-still-life) |
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## Training procedure |
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Preprocessing, hardware used, hyperparameters... |
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## Eval results |
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## Generated Images |
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You can embed local or remote images using `` |
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### BibTeX entry and citation info |
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```bibtex |
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@article{FastGAN, |
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title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis}, |
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author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal}, |
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journal={ICLR}, |
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year={2021} |
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} |
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``` |
