Chenqian Yan

725 citations
4 papers · 376 indexed · 1 hit paper · h-index 4
Topics
Neural Networks and Applications (2 papers)Face recognition and analysis (1 paper)Advanced Neural Network Applications (1 paper)
Journals
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)Journal of Image and Graphics

In The Last Decade

Chenqian Yan

4 papers receiving 371 citations

Hit Papers

Towards Optimal Structured CNN Pruning via Generative Adv...20192026202120232019100200300

Peers

Chenqian Yan
Comparison fields: 5 of 45
  • Computer Vision and Pattern Recognition 309
  • Artificial Intelligence 264
  • Electrical and Electronic Engineering 27
  • Signal Processing 22
  • Computational Mechanics 14
Replace Elad Hoffer with:
Elad Hoffer Israel
Philipp Gysel United States
Yury Nahshan Israel
Thalaiyasingam Ajanthan Australia
Yaohui Cai United States
S. Charles Brubaker United States
Jiayan Qiu Australia
Chris Ying United States
Linnan Wang United States
Chenqian Yan relative to Elad Hoffer Israel Elad Hoffer's profile →
Citations per field
00.5×3.6×
Elad Hoffer · 1×
Citations per year

Countries citing papers authored by Chenqian Yan

Since Specialization
Citations

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

Fields of papers citing papers by Chenqian Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenqian Yan

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

All Works

4 of 4 papers shown
#WorkIndexed citations
1 6
2 9
3
Dynamic Neural Network Decoupling.
3
4
Towards Optimal Structured CNN Pruning via Generative Adversarial Learningbreakdown →
358

About Chenqian Yan

Chenqian Yan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases, having authored 4 papers that have together received 376 indexed citations. Recurring topics across this work include Neural Networks and Applications (2 papers), Face recognition and analysis (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (309 citations), Computational Mathematics (6 citations) and Artificial Intelligence (264 citations). Chenqian Yan has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Rongrong Ji, Baochang Zhang, Feiyue Huang, Shaohui Lin, David Doermann, Liujuan Cao, Qixiang Ye, Xiawu Zheng, Huixia Li and Yuqing Yang. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and Journal of Image and Graphics.

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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