GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
This addresses the need for efficient GAN deployment in computer vision applications, offering a novel approach to combine multiple compression techniques, though it is incremental in building on existing methods.
The paper tackles the problem of compressing Generative Adversarial Networks (GANs) for deployment on resource-constrained devices by proposing GAN Slimming, a unified optimization framework that integrates model distillation, channel pruning, and quantization. It achieves up to 47 times compression on CartoonGAN with minimal visual quality degradation.
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile devices. Similar to other deep models, state-of-the-art GANs suffer from high parameter complexities. That has recently motivated the exploration of compressing GANs (usually generators). Compared to the vast literature and prevailing success in compressing deep classifiers, the study of GAN compression remains in its infancy, so far leveraging individual compression techniques instead of more sophisticated combinations. We observe that due to the notorious instability of training GANs, heuristically stacking different compression techniques will result in unsatisfactory results. To this end, we propose the first unified optimization framework combining multiple compression means for GAN compression, dubbed GAN Slimming (GS). GS seamlessly integrates three mainstream compression techniques: model distillation, channel pruning and quantization, together with the GAN minimax objective, into one unified optimization form, that can be efficiently optimized from end to end. Without bells and whistles, GS largely outperforms existing options in compressing image-to-image translation GANs. Specifically, we apply GS to compress CartoonGAN, a state-of-the-art style transfer network, by up to 47 times, with minimal visual quality degradation. Codes and pre-trained models can be found at https://github.com/TAMU-VITA/GAN-Slimming.