Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

Graphs have been widely adopted to denote structural connections between entities. The
relations are in many cases heterogeneous, but entangled together and denoted merely as …

Generative adversarial networks (GANs) have been effective for learning generative models
for real-world data. However, accompanied with the generative tasks becoming more and …

In this paper, we propose perceptual adversarial networks (PANs) for image-to-image
transformations. Different from existing application driven algorithms, PAN provides a …

Recent studies on face attribute transfer have achieved great success. A lot of models are
able to transfer face attributes with an input image. However, they suffer from three …

This paper studies the object transfiguration problem in wild images. The generative network
in classical GANs for object transfiguration often undertakes a dual responsibility: to detect …

The tremendous amount of accessible data in cyberspace face the risk of being
unauthorized used for training deep learning models. To address this concern, methods are …

We present a method that achieves state-of-the-art results on challenging (few-shot) layout-
to-image generation tasks by accurately modeling textures, structures and relationships …

Recently, realistic image generation using deep neural networks has become a hot topic in
machine learning and computer vision. Such an image can be generated at pixel level by …