自监督GAN压缩
Self-Supervised GAN Compression
AI总结：
本文提出一种自监督GAN压缩技术，利用训练好的判别器监督压缩生成器的训练，解决了传统权重剪枝无法直接应用于GAN的问题，并在高稀疏度下保持优异性能。
摘要：
深度学习的成功促使模型越来越大，以处理越来越复杂的任务；训练好的模型可能包含数百万个参数。这些大模型是计算和内存密集型的，这使得在最小化延迟、吞吐量和存储要求的情况下部署它们成为一项挑战。一些模型压缩方法已成功应用于图像分类和检测或语言模型，但针对执行复杂任务的生成对抗网络（GAN）进行压缩的工作非常少。在本文中，我们展示了标准的模型压缩技术（即权重剪枝）无法使用现有方法应用于GAN。随后，我们开发了一种自监督压缩技术，该技术使用训练好的判别器来监督压缩生成器的训练。我们表明，该框架在高度稀疏的情况下具有引人注目的性能，可以轻松应用于新任务和模型，并能够在不同的剪枝粒度之间进行有意义的比较。
英文摘要：
Deep learning's success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters. These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements. Some model compression methods have been successfully applied to image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks. In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods. We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator. We show that this framework has a compelling performance to high degrees of sparsity, can be easily applied to new tasks and models, and enables meaningful comparisons between different pruning granularities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
