Generative Adversarial Networks (GANs) have proven to exhibit remarkable
performance and are widely used across many generative computer vision
applications. However, the unprecedented demand for the deployment of GANs on
resource-constrained edge devices still poses a challenge due to huge number of
parameters involved in the generation process. This has led to focused
attention on the area of compressing GANs. Most of the existing works use
knowledge distillation with the overhead of teacher dependency. Moreover, there
is no ability to control the degree of compression in these methods. Hence, we
propose CoroNet-GAN for compressing GAN using the combined strength of
differentiable pruning method via hypernetworks. The proposed method provides
the advantage of performing controllable compression while training along with
reducing training time by a substantial factor. Experiments have been done on
various conditional GAN architectures (Pix2Pix and CycleGAN) to signify the
effectiveness of our approach on multiple benchmark datasets such as
Edges-to-Shoes, Horse-to-Zebra and Summer-to-Winter. The results obtained
illustrate that our approach succeeds to outperform the baselines on
Zebra-to-Horse and Summer-to-Winter achieving the best FID score of 32.3 and
72.3 respectively, yielding high-fidelity images across all the datasets.
Additionally, our approach also outperforms the state-of-the-art methods in
achieving better inference time on various smart-phone chipsets and data-types
making it a feasible solution for deployment on edge devices.

提出了一种利用可微的修剪方法通过超网络压缩 GAN 的方法，实现可控压缩训练，并在多个基准数据集上取得了优异的性能。