Sungmin Cha
Impact in
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- Image and Signal Denoising Methods
- Advanced Image Processing Techniques
- Multimodal Machine Learning Applications
Papers in
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- Advanced Image Processing Techniques 5
- Image and Signal Denoising Methods 4
- Multimodal Machine Learning Applications 3
- Advanced Neural Network Applications 2
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- Domain Adaptation and Few-Shot Learning 4
- Adversarial Robustness in Machine Learning 2
- Co-authors
- Taesup Moon (11 shared papers)Tor D. Wager (1 shared paper)Juyeon Heo (1 shared paper)Sungwoo Lee (1 shared paper)Choong‐Wan Woo (1 shared paper)Dong-Gyu Lee (1 shared paper)Sungjun Cho (2 shared papers)Youngsuk Jung (1 shared paper)
- Journals
- Nature Protocols (1 paper)ACS Applied Materials & Interfaces (1 paper)Engineering Applications of Artificial Intelligence (1 paper)IEEE Access (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- South KoreaUnited StatesCanada
In The Last Decade
Sungmin Cha
15 papers receiving 281 citations
Peers
Comparison fields: 5 of 83
- Health Informatics 10
- Computer Vision and Pattern Recognition 85
- Cognitive Neuroscience 61
- Media Technology 29
- Artificial Intelligence 88
Countries citing papers authored by Sungmin Cha
This map shows the geographic impact of Sungmin Cha'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 Sungmin Cha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sungmin Cha more than expected).
Fields of papers citing papers by Sungmin Cha
This network shows the impact of papers produced by Sungmin Cha. 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 Sungmin Cha. The network helps show where Sungmin Cha may publish in the future.
Co-authors
The 25 scholars most cited alongside Sungmin Cha, 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 | 2020 | 110 | |
| 2 | 2019 | 43 | |
| 3 | 2018 | 27 | |
| 4 | 2023 | 27 | |
| 5 | Uncertainty-based Continual Learning with Adaptive Regularization | 2019 | 18 |
| 6 | 2024 | 17 | |
| 7 | 2018 | 15 | |
| 8 | 2023 | 15 | |
| 9 | 2024 | 4 | |
| 10 | 2019 | 3 | |
| 11 | 2023 | 1 | |
| 12 | 2025 | 1 | |
| 13 | 2019 | 1 | |
| 14 | 2024 | 1 | |
| 15 | Adaptive Group Sparse Regularization for Continual Learning. | 2020 | 1 |
| 16 | 2026 | 0 |
About Sungmin Cha
Sungmin Cha is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Electrical and Electronic Engineering and Cognitive Neuroscience, having authored 16 papers that have together received 284 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (5 papers), Image and Signal Denoising Methods (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), Advanced Image Fusion Techniques (3 papers), Multimodal Machine Learning Applications (3 papers), Adversarial Robustness in Machine Learning (2 papers), Advanced Neural Network Applications (2 papers) and Membrane Separation Technologies (1 paper). The work is most often cited by research in Health Informatics (10 citations), Computer Vision and Pattern Recognition (85 citations), Cognitive Neuroscience (61 citations), Media Technology (29 citations) and Artificial Intelligence (88 citations). Sungmin Cha has collaborated with scholars based in South Korea, United States and Canada. Frequent co-authors include Taesup Moon, Tor D. Wager, Juyeon Heo, Sungwoo Lee, Choong‐Wan Woo, Dong-Gyu Lee, Sungjun Cho, Youngsuk Jung, Sohee Yang and Minjoon Seo. Their work appears in journals such as Nature Protocols, ACS Applied Materials & Interfaces, Engineering Applications of Artificial Intelligence, IEEE Access and Lecture notes in computer science.
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.