Shun Ishizaka

533 citations
2 papers · 310 indexed · 1 hit paper · h-index 2
Topics
Anomaly Detection Techniques and Applications (1 paper)Fluid Dynamics and Turbulent Flows (1 paper)Network Security and Intrusion Detection (1 paper)
Journals
Journal of Fluid Mechanics2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Partner nations
JapanFranceUnited States

In The Last Decade

Shun Ishizaka

2 papers receiving 309 citations

Hit Papers

CFLOW-AD: Real-Time Unsupervised Anomaly Detection with L...20222026202320242022100200300

Peers

Shun Ishizaka
Comparison fields: 5 of 43
  • Artificial Intelligence 248
  • Industrial and Manufacturing Engineering 84
  • Computer Vision and Pattern Recognition 82
  • Computer Networks and Communications 77
  • Control and Systems Engineering 37
Replace Denis Gudovskiy with:
Denis Gudovskiy United States
Kazuki Kozuka Japan
Tom Wehrbein Germany
Bastian Wandt Sweden
Marco Rudolph Germany
Yunkang Cao China
Hanqiu Deng Canada
Haiming Yao China
Neelu Madan Denmark
Sindy Löwe Netherlands
Shun Ishizaka relative to Denis Gudovskiy United States Denis Gudovskiy's profile →
Citations per field
00.5×1.7×
Denis Gudovskiy · 1×
Citations per year

Countries citing papers authored by Shun Ishizaka

Since Specialization
Citations

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

Fields of papers citing papers by Shun Ishizaka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shun Ishizaka

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

All Works

2 of 2 papers shown
#WorkIndexed citations
1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowsbreakdown →
300
2 10

About Shun Ishizaka

Shun Ishizaka is a scholar working on Signal Processing, Computational Mechanics and Computer Networks and Communications, having authored 2 papers that have together received 310 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (1 paper), Fluid Dynamics and Turbulent Flows (1 paper) and Network Security and Intrusion Detection (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (84 citations), Artificial Intelligence (248 citations) and Computer Vision and Pattern Recognition (82 citations). Shun Ishizaka has collaborated with scholars based in Japan, France and United States. Frequent co-authors include Kazuki Kozuka, Denis Gudovskiy, Yuji Tasaka and Jorge Peixinho. Their work appears in journals such as Journal of Fluid Mechanics and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

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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