25 citations · 62 across the 11 of their papers we have counts for
11 papers
Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks
Xin Ding, Yongwei Wang, Zuheng Xu
Continuous Conditional Generative Adversarial Networks (CcGANs) enable generative modeling conditional on continuous scalar variables (termed regression labels). However, they can…
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information
Jing Fang, Yinbo Yu, Zhongyuan Wang +2
Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images. Spatial-domain information has been widely exploited to impleme…
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework
Li Ding, Yongwei Wang, Xin Ding +4
Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images. To defend against adversarial…
Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression
Xin Ding, Yongwei Wang, Zuheng Xu +2
Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student based on the knowledge from a…
Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label
Wu Wang, Yue Huang, Xinhao Ding
Hyperspectral image fusion (HIF) is critical to a wide range of applications in remote sensing and many computer vision applications. Most traditional HIF methods assume that the o…
Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable
Xin Ding, Yongwei Wang, Z. Jane Wang +1
Recently, subsampling or refining images generated from unconditional GANs has been actively studied to improve the overall image quality. Unfortunately, these methods are often ob…