Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

Although generative adversarial networks (GANs) have made significant progress in face
synthesis, there lacks enough understanding of what GANs have learned in the latent …

Generative Adversarial Networks (GANs) advance face synthesis through learning the
underlying distribution of observed data. Despite the high-quality generated faces, some …

Face synthesis has achieved advanced development by using generative adversarial
networks (GANs). Existing methods typically formulate GAN as a two-player game, where a …

Generative adversarial networks (GANs) have been extensively studied in recent years and
have been used to address several problems in the fields of image generation and computer …

We apply an extension of generative adversarial networks (GANs)[8] to a conditional setting.
In the GAN framework, a “generator” network is tasked with fooling a “discriminator” network …

In recent years, powerful generative adversarial networks (GAN) have been developed to
automatically synthesize realistic images from text. However, most existing tasks are limited …

Generative adversarial networks (GANs) have been extensively studied in the past few
years. Arguably their most significant impact has been in the area of computer vision where …

Abstract Generative Adversarial Networks (GANs) have made a dramatic leap in high-fidelity
image synthesis and stylized face generation. Recently, a layer-swapping mechanism has …

Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity
image synthesis, there lacks enough understanding of how GANs are able to map a latent …