Synthesizing high-quality realistic images from text descriptions is a challenging task.
Existing text-to-image Generative Adversarial Networks generally employ a stacked …

Unsupervised image-to-image translation tasks aim to find a mapping between a source
domain X and a target domain Y from unpaired training data. Contrastive learning for …

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which
allows attention-driven, long-range dependency modeling for image generation tasks …

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 …

Among the major remaining challenges for generative adversarial networks (GANs) is the
capacity to synthesize globally and locally coherent images with object shapes and textures …

One of the challenges in the study of generative adversarial networks is the instability of its
training. In this paper, we propose a novel weight normalization technique called spectral …

Generative adversarial networks (GANs) have become increasingly powerful, generating
mind-blowing photorealistic images that mimic the content of datasets they have been …

Generative Adversarial Networks (GANs) are known to be difficult to train, despite
considerable research effort. Several regularization techniques for stabilizing training have …

In most existing learning systems, images are typically viewed as 2D pixel arrays. However,
in another paradigm gaining popularity, a 2D image is represented as an implicit neural …

Recent years have witnessed the rapid progress of generative adversarial networks (GANs).
However, the success of the GAN models hinges on a large amount of training data. This …