1 code implementation • 26 Mar 2024 • Dihan Zheng, Yihang Zou, Xiaowen Zhang, Chenglong Bao
We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.
no code implementations • 27 Sep 2023 • Xuanlong Yu, Yi Zuo, Zitao Wang, Xiaowen Zhang, Jiaxuan Zhao, Yuting Yang, Licheng Jiao, Rui Peng, Xinyi Wang, Junpei Zhang, Kexin Zhang, Fang Liu, Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo, Hanlin Tian, Kenta Matsui, Tianhao Wang, Fahmy Adan, Zhitong Gao, Xuming He, Quentin Bouniot, Hossein Moghaddam, Shyam Nandan Rai, Fabio Cermelli, Carlo Masone, Andrea Pilzer, Elisa Ricci, Andrei Bursuc, Arno Solin, Martin Trapp, Rui Li, Angela Yao, Wenlong Chen, Ivor Simpson, Neill D. F. Campbell, Gianni Franchi
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
no code implementations • 21 Jul 2023 • Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany, Jilei Yang, Yuliia Lut, Rahul Tandra, Xiaowen Zhang, Xinyi Zheng, Zach Douglas, Vidita Nolkha, Parvez Ahammad, Gennady Samorodnitsky
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies.
no code implementations • 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) 2023 • Xiaowen Zhang, Ying Zhou, Shin Hwei Tan
Second, we evaluate Codegex in automated code review by running it on 4256 PRs where it generated 372 review comments and received 116 feedback.
1 code implementation • 17 Oct 2022 • Furkan Kınlı, Sami Menteş, Barış Özcan, Furkan Kıraç, Radu Timofte, Yi Zuo, Zitao Wang, Xiaowen Zhang, Yu Zhu, Chenghua Li, Cong Leng, Jian Cheng, Shuai Liu, Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei, Tianzhi Ma, Zihan Gao, Wenxin He, Woon-Ha Yeo, Wang-Taek Oh, Young-Il Kim, Han-Cheol Ryu, Gang He, Shaoyi Long, S. M. A. Sharif, Rizwan Ali Naqvi, Sungjun Kim, Guisik Kim, Seohyeon Lee, Sabari Nathan, Priya Kansal
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.
1 code implementation • 26 Sep 2022 • Mengli Cheng, Yue Gao, Guoqiang Liu, Hongsheng Jin, Xiaowen Zhang
We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems.
1 code implementation • 21 Apr 2022 • Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, Chenglong Bao
Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data.
no code implementations • 29 Sep 2021 • Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, Chenglong Bao
Collecting the paired training data is a difficult task in practice, but the unpaired samples broadly exist.
1 code implementation • 29 Jun 2021 • Yuntao Du, Yinghao Chen, Fengli Cui, Xiaowen Zhang, Chongjun Wang
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain.
1 code implementation • ICLR 2021 • Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao
In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance.
no code implementations • 18 Nov 2020 • Yayuan Qin, Yao Shen, ChangLe Liu, Hongliang Wo, Yonghao Gao, Yu Feng, Xiaowen Zhang, Gaofeng Ding, Yiqing Gu, Qisi Wang, Shoudong Shen, Helen C. Walker, Robert Bewley, Jianhui Xu, Martin Boehm, Paul Steffens, Seiko Ohira-Kawamura, Naoki Murai, Astrid Schneidewind, Xin Tong, Gang Chen, Jun Zhao
We report thermodynamic and neutron scattering measurements of the triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic fields.
Strongly Correlated Electrons Materials Science
no code implementations • 26 Mar 2020 • Yuntao Du, Ruiting Zhang, Xiaowen Zhang, Yirong Yao, Hengyang Lu, Chongjun Wang
In this paper, a novel method called \textit{learning TransFerable and Discriminative Features for unsupervised domain adaptation} (TFDF) is proposed to optimize these two objectives simultaneously.
1 code implementation • 1 Jan 2020 • Yuntao Du, Zhiwen Tan, Qian Chen, Xiaowen Zhang, Yirong Yao, Chongjun Wang
Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains.
Ranked #5 on Domain Adaptation on ImageCLEF-DA