geninhu's picture
  - 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 272 high-quality images of aurora.

How to use

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Limitations and bias

  • Converge faster and better with small datasets (less than 1000 samples)

Training data


Generated Images

Example image

BibTeX entry and citation info

  title={Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis},
  author={Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal},