Janne Hellsten
Impact in
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- Computer Graphics and Visualization Techniques
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- Generative Adversarial Networks and Image Synthesis
- Advanced Image Processing Techniques
- Digital Media Forensic Detection
- Face recognition and analysis
- Advanced Vision and Imaging
- Advanced Image and Video Retrieval Techniques
Papers in
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- Generative Adversarial Networks and Image Synthesis 3
- Advanced Image and Video Retrieval Techniques 1
- Image Processing and 3D Reconstruction 1
- Digital Media Forensic Detection 1
- Advanced Neural Network Applications 1
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- Computer Graphics and Visualization Techniques 1
- Co-authors
- Samuli Laine (4 shared papers)Jaakko Lehtinen (5 shared papers)Timo Aila (5 shared papers)Tero Karras (5 shared papers)Miika Aittala (4 shared papers)Yeongho Seol (1 shared paper)Ting-Chun Wang (1 shared paper)Tim Brooks (1 shared paper)
- Journals
- ACM Transactions on Graphics (1 paper)Neural Information Processing Systems (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United KingdomFinlandUnited States
In The Last Decade
Janne Hellsten
5 papers receiving 189 citations
Peers
Comparison fields: 5 of 65
- Computer Graphics and Computer-Aided Design 31
- Computer Vision and Pattern Recognition 134
- Health Informatics 2
- Artificial Intelligence 40
- Biophysics 6
Countries citing papers authored by Janne Hellsten
This map shows the geographic impact of Janne Hellsten'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 Janne Hellsten with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Janne Hellsten more than expected).
Fields of papers citing papers by Janne Hellsten
This network shows the impact of papers produced by Janne Hellsten. 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 Janne Hellsten. The network helps show where Janne Hellsten may publish in the future.
Co-authors
The 8 scholars most cited alongside Janne Hellsten, 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 | 91 | |
| 2 | Training Generative Adversarial Networks with Limited Data | 2020 | 56 |
| 3 | 2024 | 36 | |
| 4 | 2020 | 13 | |
| 5 | 2022 | 1 |
About Janne Hellsten
Janne Hellsten is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Statistical and Nonlinear Physics, Computational Mechanics and Insect Science, having authored 5 papers that have together received 197 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (3 papers), Advanced Image and Video Retrieval Techniques (1 paper), Model Reduction and Neural Networks (1 paper), Image Processing and 3D Reconstruction (1 paper), Computer Graphics and Visualization Techniques (1 paper), 3D Shape Modeling and Analysis (1 paper), Digital Media Forensic Detection (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Graphics and Computer-Aided Design (31 citations), Computer Vision and Pattern Recognition (134 citations), Health Informatics (2 citations), Artificial Intelligence (40 citations) and Biophysics (6 citations). Janne Hellsten has collaborated with scholars based in United Kingdom, Finland and United States. Frequent co-authors include Samuli Laine, Jaakko Lehtinen, Timo Aila, Tero Karras, Miika Aittala, Yeongho Seol, Ting-Chun Wang and Tim Brooks. Their work appears in journals such as ACM Transactions on Graphics, Neural Information Processing Systems 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.