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.
Input feature selection for classification problems
This map shows the geographic impact of Nojun Kwak'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 Nojun Kwak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nojun Kwak more than expected).
This network shows the impact of papers produced by Nojun Kwak. 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 Nojun Kwak. The network helps show where Nojun Kwak may publish in the future.
Co-authorship network of co-authors of Nojun Kwak
This figure shows the co-authorship network connecting the top 25 collaborators of Nojun Kwak.
A scholar is included among the top collaborators of Nojun Kwak 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 Nojun Kwak. Nojun Kwak is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kwak, Nojun, et al.. (2020). Feature-map-level Online Adversarial Knowledge Distillation. Seoul National University Open Repository (Seoul National University). 1. 2006–2015.2 indexed citations
Jeong, Jisoo, Seungeui Lee, Jeesoo Kim, & Nojun Kwak. (2019). Consistency-based Semi-supervised Learning for Object detection. Seoul National University Open Repository (Seoul National University). 32. 10758–10767.201 indexed citations
15.
Park, Sungheon & Nojun Kwak. (2018). 3D Human Pose Estimation with Relational Networks.. Seoul National University Open Repository (Seoul National University). 129.3 indexed citations
16.
Kwak, Nojun, et al.. (2018). Textbook Question Answering with Knowledge Graph Understanding and Unsupervised Open-set Text Comprehension.. arXiv (Cornell University).1 indexed citations
17.
Park, Hyojin, et al.. (2018). Concentrated-Comprehensive Convolutions for lightweight semantic segmentation. arXiv (Cornell University).5 indexed citations
18.
Park, Hyojin, Young Joon Yoo, & Nojun Kwak. (2018). MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis.. Seoul National University Open Repository (Seoul National University). 76.1 indexed citations
19.
Kwak, Nojun, et al.. (2012). Binary classification by the combination of Adaboost and feature extraction methods. Journal of the Institute of Electronics Engineers of Korea. 49(4). 42–53.2 indexed citations
20.
Park, Seunghwan & Nojun Kwak. (2010). Local Appearance-based Face Recognition Using SVM and PCA. Journal of the Institute of Electronics Engineers of Korea. 47(3). 54–60.
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.