Uiwon Hwang

660 citations
13 papers · 327 indexed · h-index 8

Impact in

    • Generative Adversarial Networks and Image Synthesis
    • Digital Media Forensic Detection
    • Advanced Image Processing Techniques
    • Advanced Neural Network Applications
    • Anomaly Detection Techniques and Applications
    • Adversarial Robustness in Machine Learning

Papers in

Uiwon Hwang

12 papers receiving 315 citations

Peers

Uiwon Hwang
Comparison fields: 5 of 78
  • Computer Vision and Pattern Recognition 141
  • Artificial Intelligence 146
  • Signal Processing 47
  • Media Technology 24
  • Health Informatics 3
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Citations per field
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Citations per year

Countries citing papers authored by Uiwon Hwang

Since Specialization
Citations

This map shows the geographic impact of Uiwon Hwang'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 Uiwon Hwang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Uiwon Hwang more than expected).

Fields of papers citing papers by Uiwon Hwang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Uiwon Hwang. 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 Uiwon Hwang. The network helps show where Uiwon Hwang may publish in the future.

Co-authors

The 16 scholars most cited alongside Uiwon Hwang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Uiwon Hwang Line = papers co-authored together Uiwon Hwang links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2019214
2 201939
3
How Generative Adversarial Networks and its variants Work: An Overview of GAN
201717
4 202111
5 202311
6 20199
7
Disease Prediction from Electronic Health Records Using Generative Adversarial Networks.
20178
8 20237
9
A SeqGAN for Polyphonic Music Generation.
20174
10
Memory-Augmented Neural Networks for Knowledge Tracing from the Perspective of Learning and Forgetting
20184
11
How Generative Adversarial Networks and Their Variants Work: An Overview of GAN
20172
12 20241
13 20240

About Uiwon Hwang

Uiwon Hwang is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Science Applications and Biophysics, having authored 13 papers that have together received 327 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (5 papers), Adversarial Robustness in Machine Learning (4 papers), Machine Learning in Healthcare (2 papers), AI in cancer detection (2 papers), Music Technology and Sound Studies (2 papers), Music and Audio Processing (2 papers), Anomaly Detection Techniques and Applications (2 papers) and Digital Media Forensic Detection (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (141 citations), Artificial Intelligence (146 citations), Signal Processing (47 citations), Media Technology (24 citations) and Health Informatics (3 citations). Uiwon Hwang has collaborated with scholars based in South Korea, Puerto Rico and Japan. Frequent co-authors include Sungroh Yoon, Yongjun Hong, Jaeyoon Yoo, Jaewoo Park, Nam Ik Cho, Sung‐Woon Choi, Yo-Han Kim, Yunheung Paek, Ho Bae and Seonwoo Min. Their work appears in journals such as IEEE Access, Artificial Intelligence in Medicine, ACM Computing Surveys, IEEE Transactions on Artificial Intelligence and Journal of Mechanical Science and Technology.

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.

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