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Jiale Chen

Publications
5
Citations
139
Publications by Year
20172021
Co-Authors:
Show All Co-Authors (71)
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Jiale Chen

Affiliation

CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System
University of Science and Technology of China
Hefei, China

Publication Topics

Biography

Jiale Chen received the B.S. degree in communication engineering from Zhengzhou University, Zhengzhou, China. He is currently pursuing the M.S. degree in electronic engineering from the University of Science and Technology of China, Hefei, China.,His current research interests include reinforcement learning and media processing.(Based on document published on 31 December 2019).
Publications
5
Citations
139
Publications by Year
20172021
Co-Authors:
Show All Co-Authors (71)
Author's Published Works

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Asynchronous Episodic Deep Deterministic Policy Gradient: Toward Continuous Control in Computationally Complex Environments

Zhizheng Zhang;Jiale Chen;Zhibo Chen;Weiping Li

IEEE Transactions on Cybernetics
Year: 2021 | Volume: 51, Issue: 2 | Journal Article |
Cited by: Papers (40)
Deep deterministic policy gradient (DDPG) has been proved to be a successful reinforcement learning (RL) algorithm for continuous control tasks. However, DDPG still suffers from data insufficiency and training inefficiency, especially, in computationally complex environments. In this article, we propose asynchronous episodic DDPG (AE-DDPG), as an expansion of DDPG, which can achieve more effective...Show More

Asynchronous Episodic Deep Deterministic Policy Gradient: Toward Continuous Control in Computationally Complex Environments

Zhizheng Zhang;Jiale Chen;Zhibo Chen;Weiping Li

Year: 2021 | Volume: 51, Issue: 2 | Journal Article |

AI-GAN: Signal De-Interference via Asynchronous Interactive Generative Adversarial Network

Xin Jin;Zhibo Chen;Jianxin Lin;Wei Zhou;Jiale Chen;Chaowei Shan

2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Year: 2019 | Conference Paper |
Cited by: Papers (2)
Interfering signals, such as rain streak, haze, noise, etc, introduce various types of visibility degradation on original clean signals. Traditional algorithms tackle the signal de-interference problem by the way of signal removal, which usually causes over-smoothness and unexpected artifacts. Hereby, this paper attempts to solve this problem from a totally different perspective of signal decompos...Show More

AI-GAN: Signal De-Interference via Asynchronous Interactive Generative Adversarial Network

Xin Jin;Zhibo Chen;Jianxin Lin;Wei Zhou;Jiale Chen;Chaowei Shan

Year: 2019 | Conference Paper |

Interleaved Zooming Network for Image Inpainting

Sen Liu;Zongyu Guo;Jiale Chen;Tao Yu;Zhibo Chen

2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Recently, deep learning-based techniques have shown great potential for image inpainting tasks thanks to the powerful representation ability of convolutional neural networks. However, current approaches failed to inpaint satisfactorily in both global structures and local textures, because they only use simple dilated convolution which hard to encode comprehensive deep features. In this paper, we p...Show More

Interleaved Zooming Network for Image Inpainting

Sen Liu;Zongyu Guo;Jiale Chen;Tao Yu;Zhibo Chen

Year: 2019 | Conference Paper |

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

Year: 2018 | Conference Paper |

Gender classification in live videos

Jiale Chen;Sen Liu;Zhibo Chen

2017 IEEE International Conference on Image Processing (ICIP)
Year: 2017 | Conference Paper |
Cited by: Papers (7)
Human facial gender classification is an important task in live videos. However, it is still challenging in real applications due to motion blur, object occlusion and extreme illumination in real live videos. In this paper, we propose the Multi-Branch Voting CNN (MBV-CNN) framework which first detects and extracts the human face images in live videos, then apply adaptive brightness enhancement on ...Show More

Gender classification in live videos

Jiale Chen;Sen Liu;Zhibo Chen

Year: 2017 | Conference Paper |

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