Content-Aware GAN Compression

@article{Liu2021ContentAwareGC,
  title={Content-Aware GAN Compression},
  author={Yuchen Liu and Zhixin Shu and Yijun Li and Zhe L. Lin and Federico Perazzi and S. Y. Kung},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={12151-12161}
}
  • Yuchen Liu, Zhixin Shu, S. Kung
  • Published 6 April 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders their efficient deployment on edge devices. Directly applying generic compression approaches yields poor results on GANs, which motivates a number of recent GAN compression works. While prior works mainly accelerate conditional GANs, e.g., pix2pix and Cycle-GAN, compressing state-of-the-art unconditional GANs has rarely… 

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