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.

翻译：生成对抗网络（GANs）已被证明具有显著的性能，并被广泛应用于许多生成式计算机视觉应用中。然而，由于生成过程中涉及大量参数，在资源受限的边缘设备上部署GANs的前所未有的需求仍然构成挑战。这促使人们将注意力集中在压缩GANs的领域。现有的大多数工作依赖于知识蒸馏，存在教师依赖的开销。此外，这些方法无法控制压缩的程度。因此，我们提出CoroNet-GAN，通过超网络结合可微剪枝方法的联合优势来压缩GAN。所提出的方法在提供可控压缩能力的同时，还能显著缩短训练时间。我们在多种条件GAN架构（Pix2Pix和CycleGAN）上进行了实验，以证明我们的方法在多个基准数据集（如Edges-to-Shoes、Horse-to-Zebra和Summer-to-Winter）上的有效性。结果表明，我们的方法在Zebra-to-Horse和Summer-to-Winter任务上超越了基线，分别取得了32.3和72.3的最佳FID分数，并在所有数据集上生成了高保真图像。此外，我们的方法在多种智能手机芯片组和数据类型上实现了更优的推理时间，超越了现有最优方法，使其成为在边缘设备上部署的可行解决方案。