Search Results for author:
Found 7 papers, 4 papers with code
Real or Not Real, that is the Question
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 random variable that can be estimated from multiple angles.
Improving On-policy Learning with Statistical Reward Accumulation
Most methods in deep-RL achieve good results via the maximization of the reward signal provided by the environment, typically in the form of discounted cumulative returns.
Aesthetic-Driven Image Enhancement by Adversarial Learning
We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement.
Image Aesthetic Assessment: An Experimental Survey
This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality.
Boosting Optical Character Recognition: A Super-Resolution Approach
Text image super-resolution is a challenging yet open research problem in the computer vision community.
Compression Artifacts Reduction by a Deep Convolutional Network
Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.
Learning to Recognize Pedestrian Attribute
Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given.
