Jae-Hyun Jun
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Internet Traffic Analysis and Secure E-voting
- Machine Learning and ELM
- Ophthalmology top 10%
- Retinal Diseases and Treatments
Papers in
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- Image and Video Quality Assessment 2
- Multimodal Machine Learning Applications 2
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- Network Security and Intrusion Detection 3
- Co-authors
- Jung-Woo Ha (1 shared paper)Jin-Hwa Kim (1 shared paper)Sang-Woo Lee (1 shared paper)Byoung‐Tak Zhang (2 shared papers)Hak Seung Lee (1 shared paper)Yeong Shik Kim (1 shared paper)Bon‐Kwon Koo (1 shared paper)Sung‐Ho Kim (9 shared papers)
- Journals
- Molecules (1 paper)Journal of Information Processing Systems (1 paper)Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong (1 paper)International Joint Conference on Artificial Intelligence (1 paper)Journal of the Korea Academia-Industrial cooperation Society (1 paper)
- Partner nations
- South Korea
In The Last Decade
Jae-Hyun Jun
12 papers receiving 244 citations
Peers
Comparison fields: 5 of 73
- Artificial Intelligence 133
- Ophthalmology 31
- Computer Vision and Pattern Recognition 79
- Biochemistry 20
- Signal Processing 25
Countries citing papers authored by Jae-Hyun Jun
This map shows the geographic impact of Jae-Hyun Jun'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 Jae-Hyun Jun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jae-Hyun Jun more than expected).
Fields of papers citing papers by Jae-Hyun Jun
This network shows the impact of papers produced by Jae-Hyun Jun. 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 Jae-Hyun Jun. The network helps show where Jae-Hyun Jun may publish in the future.
Co-authors
The 14 scholars most cited alongside Jae-Hyun Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 120 | |
| 2 | 2014 | 85 | |
| 3 | 2014 | 22 | |
| 4 | 2011 | 13 | |
| 5 | 2017 | 4 | |
| 6 | 2010 | 2 | |
| 7 | 2010 | 2 | |
| 8 | 2014 | 2 | |
| 9 | Criteria for Human-Compatible AI in Two-Player Vision-Language Tasks. | 2017 | 1 |
| 10 | Enhanced H.264 Coding Method Applied to Adaptive Block Search Algorithm | 2011 | 1 |
| 11 | 2014 | 1 | |
| 12 | 2020 | 1 | |
| 13 | 2015 | 0 | |
| 14 | 2012 | 0 | |
| 15 | 2015 | 0 |
About Jae-Hyun Jun
Jae-Hyun Jun is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications, Artificial Intelligence, Signal Processing and Sociology and Political Science, having authored 15 papers that have together received 254 indexed citations. Recurring topics across this work include Network Security and Intrusion Detection (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Video Coding and Compression Technologies (2 papers), Network Packet Processing and Optimization (2 papers), Internet Traffic Analysis and Secure E-voting (2 papers), Image and Video Quality Assessment (2 papers), Multimedia Communication and Technology (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Artificial Intelligence (133 citations), Ophthalmology (31 citations), Computer Vision and Pattern Recognition (79 citations), Biochemistry (20 citations) and Signal Processing (25 citations). Jae-Hyun Jun has collaborated with scholars based in South Korea. Frequent co-authors include Jung-Woo Ha, Jin-Hwa Kim, Sang-Woo Lee, Byoung‐Tak Zhang, Hak Seung Lee, Yeong Shik Kim, Bon‐Kwon Koo, Sung‐Ho Kim, Hyun-Ju Oh and Min-Jun Kim. Their work appears in journals such as Molecules, Journal of Information Processing Systems, Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong, International Joint Conference on Artificial Intelligence and Journal of the Korea Academia-Industrial cooperation Society.
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.