Heewoo Jun
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Image and Video Retrieval Techniques
- Advanced Vision and Imaging
- Signal Processing top 10%
- Music and Audio Processing
- Speech and Audio Processing
Papers in
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- Speech Recognition and Synthesis 3
- Natural Language Processing Techniques 2
- Topic Modeling 2
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- Advanced Image and Video Retrieval Techniques 1
- Generative Adversarial Networks and Image Synthesis 1
- Multimodal Machine Learning Applications 1
- Co-authors
- Mark Chen (2 shared papers)Ilya Sutskever (2 shared papers)Alec Radford (2 shared papers)Rewon Child (2 shared papers)Jeffrey Wu (1 shared paper)Gregory Diamos (2 shared papers)Sercan Ö. Arık (1 shared paper)John Schulman (1 shared paper)
- Journals
- IEEE Signal Processing Letters (1 paper)International Conference on Machine Learning (2 papers)
- Partner nations
- United States
In The Last Decade
Heewoo Jun
4 papers receiving 365 citations
Heewoo Jun's Hit Papers
Peers
Comparison fields: 5 of 70
- Computer Vision and Pattern Recognition 258
- Signal Processing 69
- Artificial Intelligence 163
- Computer Graphics and Computer-Aided Design 17
- Media Technology 20
Countries citing papers authored by Heewoo Jun
This map shows the geographic impact of Heewoo 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 Heewoo Jun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heewoo Jun more than expected).
Fields of papers citing papers by Heewoo Jun
This network shows the impact of papers produced by Heewoo 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 Heewoo Jun. The network helps show where Heewoo Jun may publish in the future.
Co-authors
The 13 scholars most cited alongside Heewoo 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 | Generative Pretraining From Pixels Hit paper breakdown → | 2020 | 300 |
| 2 | 2018 | 71 | |
| 3 | Distribution Augmentation for Generative Modeling | 2020 | 8 |
| 4 | 2019 | 3 |
About Heewoo Jun
Heewoo Jun is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Infectious Diseases and Organic Chemistry, having authored 4 papers that have together received 382 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (3 papers), Natural Language Processing Techniques (2 papers), Topic Modeling (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Speech and Audio Processing (1 paper), Music and Audio Processing (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (258 citations), Signal Processing (69 citations), Artificial Intelligence (163 citations), Computer Graphics and Computer-Aided Design (17 citations) and Media Technology (20 citations). Heewoo Jun has collaborated with scholars based in United States. Frequent co-authors include Mark Chen, Ilya Sutskever, Alec Radford, Rewon Child, Jeffrey Wu, Gregory Diamos, Sercan Ö. Arık, John Schulman, Jiaji Huang and Mostofa Patwary. Their work appears in journals such as IEEE Signal Processing Letters and International Conference on Machine Learning.
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.