Heewoo Jun
Impact in
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- Advanced Neural Network Applications
- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Artificial Intelligence top 5%
- Speech Recognition and Synthesis
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Natural Language Processing Techniques
Papers in
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- Speech Recognition and Synthesis 3
- Topic Modeling 2
- Adversarial Robustness in Machine Learning 1
- Natural Language Processing Techniques 1
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- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Anuroop Sriram (2 shared papers)Sanjeev Satheesh (2 shared papers)Rewon Child (2 shared papers)Ilya Sutskever (2 shared papers)Alec Radford (2 shared papers)Mark I-Cheng Chen (2 shared papers)Jeffrey Wu (1 shared paper)Adam Coates (1 shared paper)
- Journals
- IEEE Signal Processing Letters (1 paper)Proceedings - IEEE International Parallel and Distributed Processing Symposium (1 paper)International Conference on Machine Learning (2 papers)IEEE International Conference on Acoustics Speech and Signal Processing (1 paper)Interspeech (USB) (1 paper)
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Heewoo Jun
6 papers receiving 609 citations
Heewoo Jun's Hit Papers
Peers
Comparison fields: 5 of 52
- Computer Vision and Pattern Recognition 244
- Artificial Intelligence 315
- Signal Processing 48
- Media Technology 12
- Human-Computer Interaction 9
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 16 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 | 328 |
| 2 | 2018 | 184 | |
| 3 | 2018 | 80 | |
| 4 | 2018 | 41 | |
| 5 | Distribution Augmentation for Generative Modeling | 2020 | 8 |
| 6 | 2019 | 3 |
About Heewoo Jun
Heewoo Jun is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Nuclear Energy and Engineering, Waste Management and Disposal and Infectious Diseases, having authored 6 papers that have together received 644 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (3 papers), Topic Modeling (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Adversarial Robustness in Machine Learning (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (244 citations), Artificial Intelligence (315 citations), Signal Processing (48 citations), Media Technology (12 citations) and Human-Computer Interaction (9 citations). Heewoo Jun has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Anuroop Sriram, Sanjeev Satheesh, Rewon Child, Ilya Sutskever, Alec Radford, Mark I-Cheng Chen, Jeffrey Wu, Adam Coates, Sercan Ö. Arık and Gregory Diamos. Their work appears in journals such as IEEE Signal Processing Letters, Proceedings - IEEE International Parallel and Distributed Processing Symposium, International Conference on Machine Learning, IEEE International Conference on Acoustics Speech and Signal Processing and Interspeech (USB).
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.