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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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[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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[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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This model was trained on a dataset of 124 high-quality Fauvism painting images.
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#### How to use
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[few-shot-fauvism-still-life](https://huggingface.co/datasets/huggan/few-shot-fauvism-still-life)
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## Generated Images
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