Yen-Chang Hsu
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Signal Processing
- Media Technology
- Co-authors
- Zsolt KiraYilin ShenHongxia JinJames SmithZhaoyang LvAvik RayZheng XuShangqian Gao
- Topics
- Domain Adaptation and Few-Shot Learning (8 papers)Multimodal Machine Learning Applications (6 papers)Topic Modeling (3 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)arXiv (Cornell University)
- Partner nations
- United StatesUnited KingdomJapan
In The Last Decade
Yen-Chang Hsu
15 papers receiving 224 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 182
- Computer Vision and Pattern Recognition 127
- Radiology, Nuclear Medicine and Imaging 21
- Signal Processing 10
- Media Technology 10
Countries citing papers authored by Yen-Chang Hsu
This map shows the geographic impact of Yen-Chang Hsu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yen-Chang Hsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yen-Chang Hsu more than expected).
Fields of papers citing papers by Yen-Chang Hsu
This network shows the impact of papers produced by Yen-Chang Hsu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yen-Chang Hsu. The network helps show where Yen-Chang Hsu may publish in the future.
Co-authorship network of co-authors of Yen-Chang Hsu
This figure shows the co-authorship network connecting the top 25 collaborators of Yen-Chang Hsu. A scholar is included among the top collaborators of Yen-Chang Hsu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yen-Chang Hsu. Yen-Chang Hsu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 18 | |
| 5 | 2 | |
| 6 | 29 | |
| 7 | 2 | |
| 8 | 4 | |
| 9 | 23 | |
| 10 | 17 | |
| 11 | 3 | |
| 12 | 89 | |
| 13 | 8 | |
| 14 | Training Student Networks for Acceleration with Conditional Adversarial Networks. | 12 |
| 15 | Learning to cluster in order to transfer across domains and tasks | 17 |
| 16 | Learning Loss for Knowledge Distillation with Conditional Adversarial Networks | 6 |
About Yen-Chang Hsu
Yen-Chang Hsu is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 16 papers that have together received 233 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (6 papers) and Topic Modeling (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (127 citations), Artificial Intelligence (182 citations) and Computational Mathematics (2 citations). Yen-Chang Hsu has collaborated with scholars based in United States, United Kingdom and Japan. Frequent co-authors include Zsolt Kira, Yilin Shen, Hongxia Jin, James Smith, Zhaoyang Lv, Avik Ray, Zheng Xu, Shangqian Gao, Minchul Kim and Jiawei Huang. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and arXiv (Cornell University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.