no code implementations • 5 May 2024 • Xiaole Tang, Xin Hu, Xiang Gu, Jian Sun
In this work, we propose a novel Residual-Conditioned Optimal Transport (RCOT) approach, which models image restoration as an optimal transport (OT) problem for both unpaired and paired settings, introducing the transport residual as a unique degradation-specific cue for both the transport cost and the transport map.
1 code implementation • 26 Apr 2024 • Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu
To theoretically analyze our method, we deduce an upper bound of target domain expected error for PDA, which is approximately minimized in our approach.
1 code implementation • 2 Nov 2023 • Xiang Gu, Liwei Yang, Jian Sun, Zongben Xu
Conditional score-based diffusion model (SBDM) is for conditional generation of target data with paired data as condition, and has achieved great success in image translation.
2 code implementations • 23 Mar 2023 • Xiang Gu, Yucheng Yang, Wei Zeng, Jian Sun, Zongben Xu
In this paper, we propose a novel KeyPoint-Guided model by ReLation preservation (KPG-RL) that searches for the optimal matching (i. e., transport plan) guided by the keypoints in OT.
no code implementations • ICCV 2023 • Zixiang Zhao, Jiangshe Zhang, Xiang Gu, Chengli Tan, Shuang Xu, Yulun Zhang, Radu Timofte, Luc van Gool
Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features.
1 code implementation • 3 Mar 2023 • Liwei Yang, Xiang Gu, Jian Sun
SSDP aims to reduce domain gap by projecting data to the source domain, while MLCL is a learning scheme to learn discriminative and generalizable features on the projected data.
1 code implementation • NeurIPS 2021 • Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on Partial Domain Adaptation on DomainNet
no code implementations • 1 Jan 2021 • Xiang Gu, Jiasun Feng, Jian Sun, Zongben Xu
In this framework, we model the domain generalization as a learning problem that enforces the learner to be able to generalize well for any train/val subsets splitting of the training dataset.
1 code implementation • CVPR 2020 • Xiang Gu, Jian Sun, Zongben Xu
In this paper, we propose a novel adversarial DA approach completely defined in spherical feature space, in which we define spherical classifier for label prediction and spherical domain discriminator for discriminating domain labels.
1 code implementation • 1 Mar 2019 • Xiang Gu, Sina Ghiassian, Richard S. Sutton
ETD was proposed mainly to address convergence issues of conventional Temporal Difference (TD) learning under off-policy training but it is different from conventional TD learning even under on-policy training.