8 code implementations • 14 Sep 2020 • Zi-Yu Wan, Bo Zhang, Dong-Dong Chen, Pan Zhang, Dong Chen, Jing Liao, Fang Wen
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
no code implementations • 4 Jun 2020 • Yun Luo, Li-Zhen Zhu, Zi-Yu Wan, Bao-liang Lu
Then, we augment the original training datasets with a different number of generated realistic-like EEG data.
7 code implementations • CVPR 2020 • Zi-Yu Wan, Bo Zhang, Dong-Dong Chen, Pan Zhang, Dong Chen, Jing Liao, Fang Wen
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
1 code implementation • NeurIPS 2019 • Zi-Yu Wan, Dong-Dong Chen, Yan Li, Xingguang Yan, Junge Zhang, Yizhou Yu, Jing Liao
Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i. e. alleviate the above domain shift problem).