Content-Aware GAN Compression

• URL: http://arxiv.org/abs/2104.02244v1
• Date: Tue, 6 Apr 2021 02:23:56 GMT
• Title: Content-Aware GAN Compression
• Authors: Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Federico Perazzi, S.Y. Kung
• Abstract summary: Generative adversarial networks (GANs) play a vital role in various image generation and synthesis tasks. Their notoriously high computational cost hinders their efficient deployment on edge devices. We propose novel approaches for unconditional GAN compression.
• Score: 33.83749494060526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 CycleGAN, compressing state-of-the-art unconditional GANs has rarely been explored and is more challenging. In this paper, we propose novel approaches for unconditional GAN compression. We first introduce effective channel pruning and knowledge distillation schemes specialized for unconditional GANs. We then propose a novel content-aware method to guide the processes of both pruning and distillation. With content-awareness, we can effectively prune channels that are unimportant to the contents of interest, e.g., human faces, and focus our distillation on these regions, which significantly enhances the distillation quality. On StyleGAN2 and SN-GAN, we achieve a substantial improvement over the state-of-the-art compression method. Notably, we reduce the FLOPs of StyleGAN2 by 11x with visually negligible image quality loss compared to the full-size model. More interestingly, when applied to various image manipulation tasks, our compressed model forms a smoother and better disentangled latent manifold, making it more effective for image editing.

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