Generative adversarial network (GAN) is formulated as a two-player game between a
generator (G) and a discriminator (D), where D is asked to differentiate whether an image …

The success of style-based generators largely benefits from style modulation, which helps
take care of the cross-instance variation within data. However, theinstance-wise stochasticity …

In this paper, we address the challenge of compressing generative adversarial networks
(GANs) for deployment in resource-constrained environments by proposing two novel …

Various models based on StyleGAN have gained significant traction in the field of image
synthesis, attributed to their robust training stability and superior performances. Within the …

Generative adversarial networks (GANs) have achieved great success and become more
and more popular in recent years. However, understanding of the min-max game in GANs …

StyleGAN has shown remarkable performance in unconditional image generation. However
its high computational cost poses a significant challenge for practical applications. Although …

This article presents a new perspective from control theory to interpret and solve the
instability and mode collapse problems of generative adversarial networks (GANs). The …

生成对抗网络(Generative adversarial network, GAN) 由生成模型和判别模型构成,
生成模型获取真实数据的概率分布, 判别模型判断输入是真实数据还是生成器生成的数据 …

Abstract Generative Adversarial Networks (GANs) have received considerable attention due
to its outstanding ability to generate images. However, training a GAN is hard since the …

Recent years have seen significant advances in image inpainting for any shape of missing
regions. However, the performance of existing methods degrades drastically when …