Labeling images for classification can be expensive. Semi-Supervised Learning (SSL)
Generative Adversarial Network (GAN) methods train good classifiers with a few labeled …

It can be expensive to label images for classification. Good classifiers or high-quality images
can be trained on unlabeled data with Generative Adversarial Network (GAN) methods. We …

Recently, a number of papers have addressed the theory and applications of the Generative
Adversarial Network (GAN) in various fields of image processing. Fewer studies, however …

The emerging of Generative Adversarial Networks (GANs) gives rise to a significant
improvement in image generation. However, a controllable way of synthesizing images with …

In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial
Network,(TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing …

Abstract Although Generative Adversarial Networks (GAN) have shown remarkable
performance in image generation, there exist some challenges in instability and …

Abstract Generative Adversarial Networks have demonstrated potential on a variety of
generative tasks, although they are regarded as unstable and sometimes they miss modes …

Existing image generation tasks produce blurry, unrealistic results and images that lack
layers and structure. Depth information can be used to accurately control the relative …

Abstract Traditional Generative Adversarial Network (GAN) ignores the representation and
structural information of the original feature when the feature map is large, and there is no …

Generative adversarial networks (GANs) represent a significant advance in the field of deep
learning for image generation problems. With their ability to generate highly realistic and …