Fei Ye
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Cardiology and Cardiovascular Medicine top 10%
- Biomedical Engineering
- Radiology, Nuclear Medicine and Imaging
- Topics
- Domain Adaptation and Few-Shot Learning (25 papers)Generative Adversarial Networks and Image Synthesis (14 papers)Anomaly Detection Techniques and Applications (8 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Fei Ye
52 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Artificial Intelligence 516
- Computer Vision and Pattern Recognition 281
- Cardiology and Cardiovascular Medicine 142
- Biomedical Engineering 112
- Radiology, Nuclear Medicine and Imaging 89
Countries citing papers authored by Fei Ye
This map shows the geographic impact of Fei Ye'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 Fei Ye with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fei Ye more than expected).
Fields of papers citing papers by Fei Ye
This network shows the impact of papers produced by Fei Ye. 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 Fei Ye. The network helps show where Fei Ye may publish in the future.
Co-authorship network of co-authors of Fei Ye
This figure shows the co-authorship network connecting the top 25 collaborators of Fei Ye. A scholar is included among the top collaborators of Fei Ye 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 Fei Ye. Fei Ye is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 26 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 0 | |
| 10 | 4 | |
| 11 | 0 | |
| 12 | 7 | |
| 13 | 2 | |
| 14 | 8 | |
| 15 | 6 | |
| 16 | Ecological Ethics Education Based on the Concept of Community with a Shared Future for Mankind | 0 |
| 17 | Inverse-Transform AutoEncoder for Anomaly Detection | 24 |
| 18 | 57 | |
| 19 | 30 | |
| 20 | 1 |
About Fei Ye
Fei Ye is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Health Informatics, having authored 63 papers that have together received 1.2k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (25 papers), Generative Adversarial Networks and Image Synthesis (14 papers) and Anomaly Detection Techniques and Applications (8 papers). The work is most often cited by research in Artificial Intelligence (516 citations), Computer Vision and Pattern Recognition (281 citations) and Signal Processing (78 citations). Fei Ye has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Adrian G. Borş, Bairong Shen, Fei Zhu, Quan Liu, Ya Zhang, Cewu Lu, Jinkun Cao, Chaoqin Huang, Maosen Li and Hongwei Xu. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.