Konpat Preechakul
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- Music Technology and Sound Studies 1
- Multimodal Machine Learning Applications 1
- Human Pose and Action Recognition 1
- Generative Adversarial Networks and Image Synthesis 1
- Advanced Neural Network Applications 1
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- Human Motion and Animation 1
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- Music and Audio Processing 1
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- Model Reduction and Neural Networks 2
- Co-authors
- Supasorn SuwajanakornKorrawe KarunratanakulSiyu TangEkapol ChuangsuwanichBoonserm KijsirikulSira SriswasdiThabo BeelerEmre Aksan
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignControl and Systems Engineering
- Journals
- iScience (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)
- Partner nations
- ThailandSwitzerlandUnited States
In The Last Decade
Konpat Preechakul
4 papers receiving 208 citations
Hit Papers
Peers
Comparison fields: 5 of 45
- Computer Vision and Pattern Recognition 152
- Computer Graphics and Computer-Aided Design 16
- Control and Systems Engineering 54
- Artificial Intelligence 63
- Signal Processing 19
Countries citing papers authored by Konpat Preechakul
This map shows the geographic impact of Konpat Preechakul'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 Konpat Preechakul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Konpat Preechakul more than expected).
Fields of papers citing papers by Konpat Preechakul
This network shows the impact of papers produced by Konpat Preechakul. 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 Konpat Preechakul. The network helps show where Konpat Preechakul may publish in the future.
Co-authorship network
The 8 scholars most cited alongside Konpat Preechakul, 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 | 2024 | 9 | |
| 2 | 2023 | 49 | |
| 3 | 2022 | 16 | |
| 4 | Diffusion Autoencoders: Toward a Meaningful and Decodable Representationbreakdown → | 2022 | 137 |
About Konpat Preechakul
Konpat Preechakul is a scholar working on Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics and Signal Processing, having authored 4 papers that have together received 211 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (2 papers), Music Technology and Sound Studies (1 paper), Multimodal Machine Learning Applications (1 paper), Music and Audio Processing (1 paper), Human Pose and Action Recognition (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Human Motion and Animation (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (152 citations), Computer Graphics and Computer-Aided Design (16 citations) and Control and Systems Engineering (54 citations). Konpat Preechakul has collaborated with scholars based in Thailand, Switzerland and United States. Frequent co-authors include Supasorn Suwajanakorn, Korrawe Karunratanakul, Siyu Tang, Ekapol Chuangsuwanich, Boonserm Kijsirikul, Sira Sriswasdi, Thabo Beeler and Emre Aksan. Their work appears in journals such as iScience and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.