Generative Adversarial Networks (GANs) have shown remarkable success in
modeling complex data distributions for image-to-image translation. Still,
their high computational demands prohibit their deployment in practical
scenarios like edge devices. Existing GAN compression methods mainly rely on
knowledge distillation or convolutional classifiers' pruning techniques. Thus,
they neglect the critical characteristic of GANs: their local density structure
over their learned manifold. Accordingly, we approach GAN compression from a
new perspective by explicitly encouraging the pruned model to preserve the
density structure of the original parameter-heavy model on its learned
manifold. We facilitate this objective for the pruned model by partitioning the
learned manifold of the original generator into local neighborhoods around its
generated samples. Then, we propose a novel pruning objective to regularize the
pruned model to preserve the local density structure over each neighborhood,
resembling the kernel density estimation method. Also, we develop a
collaborative pruning scheme in which the discriminator and generator are
pruned by two pruning agents. We design the agents to capture interactions
between the generator and discriminator by exchanging their peer's feedback
when determining corresponding models' architectures. Thanks to such a design,
our pruning method can efficiently find performant sub-networks and can
maintain the balance between the generator and discriminator more effectively
compared to baselines during pruning, thereby showing more stable pruning
dynamics. Our experiments on image translation GAN models, Pix2Pix and
CycleGAN, with various benchmark datasets and architectures demonstrate our
method's effectiveness.

GAN 压缩方法从新的角度出发，通过在生成样本周围的局部邻域中保持原始模型的密度结构，提供了一种新的剪枝目标来规范剪枝模型，同时开发了一种协同剪枝方案，有效地在剪枝过程中保持生成器和判别器之间的平衡，从而展示出更稳定的剪枝动态。