Sangdoo Yun

12.1k total citations · 6 hit papers
52 papers, 5.5k citations indexed

About

Sangdoo Yun is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Sangdoo Yun has authored 52 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 30 papers in Artificial Intelligence and 3 papers in Signal Processing. Recurrent topics in Sangdoo Yun's work include Domain Adaptation and Few-Shot Learning (20 papers), Advanced Neural Network Applications (18 papers) and Video Surveillance and Tracking Methods (11 papers). Sangdoo Yun is often cited by papers focused on Domain Adaptation and Few-Shot Learning (20 papers), Advanced Neural Network Applications (18 papers) and Video Surveillance and Tracking Methods (11 papers). Sangdoo Yun collaborates with scholars based in South Korea, United States and United Kingdom. Sangdoo Yun's co-authors include Dongyoon Han, Youngjoon Yoo, Seong Joon Oh, Junsuk Choe, Sanghyuk Chun, Jin Young Choi, Byeongho Heo, Hwalsuk Lee, Jongwon Choi and Youngmin Baek and has published in prestigious journals such as Optics Express, IEEE Access and Pattern Recognition.

In The Last Decade

Sangdoo Yun

44 papers receiving 5.3k citations

Hit Papers

CutMix: Regularization Strategy to Train Strong Classifie... 2017 2026 2020 2023 2019 2019 2017 2019 2019 500 1000 1.5k 2.0k 2.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
    2019 2.6k citations Sangdoo Yun, Dongyoon Han et al. profile →
  2. Character Region Awareness for Text Detection
    2019 540 citations Dongyoon Han, Sangdoo Yun et al. profile →
  3. Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning
    2017 364 citations Sangdoo Yun, Jongwon Choi et al. profile →
  4. A Comprehensive Overhaul of Feature Distillation
    2019 357 citations Byeongho Heo, Sangdoo Yun et al. profile →
  5. What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
    2019 296 citations Jeonghun Baek, Geewook Kim et al. profile →
  6. Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
    2019 276 citations Byeongho Heo, Minsik Lee et al. profile →

Peers

Sangdoo Yun
Jianlong Fu China
Chao-Yuan Wu United States
Mohamed Omran Germany
Xiaodan Liang China
Guanbin Li China
Yabiao Wang China
Sebastian Ramos Spain
Marius Cordts Germany
Timo Rehfeld Germany
Jasper Uijlings Italy
Jianlong Fu China
Sangdoo Yun
Citations per year, relative to Sangdoo Yun Sangdoo Yun (= 1×) peers Jianlong Fu

Countries citing papers authored by Sangdoo Yun

Since Specialization
Citations

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

Fields of papers citing papers by Sangdoo Yun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sangdoo Yun

This figure shows the co-authorship network connecting the top 25 collaborators of Sangdoo Yun. A scholar is included among the top collaborators of Sangdoo Yun based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sangdoo Yun. Sangdoo Yun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Yun, Sangdoo, et al.. (2025). Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers. TUbilio (Technical University of Darmstadt). 26518–26540. 1 indexed citations
4.
Ahn, Jae‐Woo, T.H. Lee, Jin-Hwa Kim, et al.. (2024). TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models. 3291–3325.
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Lee, Hwaran, et al.. (2024). Calibrating Large Language Models Using Their Generations Only. 15440–15459. 2 indexed citations
6.
Hong, Seok‐Hee, Jae‐Woo Ahn, Hwaran Lee, et al.. (2024). Who Wrote this Code? Watermarking for Code Generation. 4890–4911. 13 indexed citations
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Han, Dongyoon, Junsuk Choe, John Joon Young Chung, et al.. (2023). Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts. 34. 20143–20155. 1 indexed citations
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Yun, Sangdoo, et al.. (2023). MPCHAT: Towards Multimodal Persona-Grounded Conversation. 3354–3377. 2 indexed citations
10.
Cha, Sungmin, et al.. (2023). Observations on K-Image Expansion of Image-Mixing Augmentation. IEEE Access. 11. 16631–16643. 1 indexed citations
11.
Yun, Sangdoo, et al.. (2023). ProPILE: Probing Privacy Leakage in Large Language Models. 20750–20762.
12.
Heo, Byeongho, Sanghyuk Chun, Seong Joon Oh, et al.. (2021). AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights. International Conference on Learning Representations. 7 indexed citations
13.
Heo, Byeongho, Sanghyuk Chun, Seong Joon Oh, et al.. (2020). Slowing Down the Weight Norm Increase in Momentum-based Optimizers. arXiv (Cornell University). 9 indexed citations
14.
Han, Dongyoon, Sangdoo Yun, Byeongho Heo, & Young Joon Yoo. (2020). ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network.. arXiv (Cornell University). 16 indexed citations
15.
Baek, Jeonghun, Geewook Kim, Junyeop Lee, et al.. (2019). What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis. 4714–4722. 296 indexed citations breakdown →
16.
Yun, Sangdoo, Dongyoon Han, Sanghyuk Chun, et al.. (2019). CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features. 6022–6031. 2641 indexed citations breakdown →
17.
Park, Hyojin, et al.. (2018). Concentrated-Comprehensive Convolutions for lightweight semantic segmentation. arXiv (Cornell University). 5 indexed citations
18.
Heo, Byeongho, Minsik Lee, Sangdoo Yun, & Jin Young Choi. (2018). Improving Knowledge Distillation with Supporting Adversarial Samples. arXiv (Cornell University). 3 indexed citations
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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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