222 citations · 555 across the 35 of their papers we have counts for
5 papers · 1 filter
Improving Generative Adversarial Networks with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu +3
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such pri…
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
Chendi Rao, Jiezhang Cao, Runhao Zeng +4
Recently, deep neural networks (DNNs) have made great progress on automated diagnosis with chest X-rays images. However, DNNs are vulnerable to adversarial examples, which may caus…
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Yong Guo, Jian Chen, Jingdong Wang +5
Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution…
Generative Low-bitwidth Data Free Quantization
Shoukai Xu, Haokun Li, Bohan Zhuang +4
Neural network quantization is an effective way to compress deep models and improve their execution latency and energy efficiency, so that they can be deployed on mobile or embedde…
Joint Wasserstein Distribution Matching
JieZhang Cao, Langyuan Mo, Qing Du +4
Joint distribution matching (JDM) problem, which aims to learn bidirectional mappings to match joint distributions of two domains, occurs in many machine learning and computer visi…