Sukmin Yun

499 citations
5 papers · 67 indexed · h-index 3
Topics
Multimodal Machine Learning Applications (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)Machine Learning and Data Classification (2 papers)
Journals
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)

In The Last Decade

Sukmin Yun

5 papers receiving 64 citations

Peers

Sukmin Yun
Comparison fields: 5 of 28
  • Artificial Intelligence 44
  • Computer Vision and Pattern Recognition 35
  • Signal Processing 5
  • Media Technology 4
  • Electrical and Electronic Engineering 4
Replace Thomas Tanay with:
Thomas Tanay Sweden
Francesco Visin United Kingdom
Ching-Yao Chuang United States
Pietro Buzzega Italy
Chen-Yu Lee Taiwan
Shagun Sodhani Canada
Jihoon Tack South Korea
Quinn Jones United States
Yujia Xie China
Junwen Pan China
Sukmin Yun relative to Thomas Tanay Sweden Thomas Tanay's profile →
Citations per field
00.5×1.5×
Thomas Tanay · 1×
Citations per year

Countries citing papers authored by Sukmin Yun

Since Specialization
Citations

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

Fields of papers citing papers by Sukmin Yun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sukmin Yun

This figure shows the co-authorship network connecting the top 25 collaborators of Sukmin Yun. A scholar is included among the top collaborators of Sukmin 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 Sukmin Yun. Sukmin Yun is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
#WorkIndexed citations
1 4
2 32
3 28
4
Regularizing Predictions via Class-wise Self-knowledge Distillation
1
5
Robust Determinantal Generative Classifier for Noisy Labels and Adversarial Attacks
2

About Sukmin Yun

Sukmin Yun is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistics, Probability and Uncertainty, having authored 5 papers that have together received 67 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (35 citations), Artificial Intelligence (44 citations) and Health Informatics (1 citation). Sukmin Yun has collaborated with scholars based in South Korea, United States and United Arab Emirates. Frequent co-authors include Jinwoo Shin, Jaehyung Kim, Bo Li, Kimin Lee, Kibok Lee, Honglak Lee, Paul Hongsuck Seo, Seong Hyeon Park and Jongjin Park. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).

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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