272 citations · 332 across the 7 of their papers we have counts for
7 papers
Real or Not Real, that is the Question
Yuanbo Xiangli, Yubin Deng, Bo Dai +2
While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a…
Improving On-policy Learning with Statistical Reward Accumulation
Yubin Deng, Ke Yu, Dahua Lin +2
Deep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided…
Aesthetic-Driven Image Enhancement by Adversarial Learning
Yubin Deng, Chen Change Loy, Xiaoou Tang
We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models i…
Image Aesthetic Assessment: An Experimental Survey
Yubin Deng, Chen Change Loy, Xiaoou Tang
This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing…
Boosting Optical Character Recognition: A Super-Resolution Approach
Chao Dong, Ximei Zhu, Yubin Deng +2
Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical opti…
Compression Artifacts Reduction by a Deep Convolutional Network
Chao Dong, Yubin Deng, Chen Change Loy +1
Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking…
Defining Chaos
Brian R. Hunt, Edward Ott
In this paper we propose, discuss and illustrate a computationally feasible definition of chaos which can be applied very generally to situations that are commonly encountered, inc…