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.

通过自我监督压缩技术，我们展示标准的模型压缩技术 (权重修剪) 不能应用于生成对抗网络 (GANs)，并且展示我们的框架在高度稀疏的情况下提供了很好的性能、易于应用于新任务和模型，并且实现了在不同压缩粒度下的有意义比较。