Deeply-Supervised Nets
In The Last Decade
doi.org/w34701519 →Countries where authors are citing Deeply-Supervised Nets
This map shows the geographic impact of Deeply-Supervised Nets. 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 Deeply-Supervised Nets with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deeply-Supervised Nets more than expected).
Fields of papers citing Deeply-Supervised Nets
This network shows the impact of Deeply-Supervised Nets. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deeply-Supervised Nets.
About Deeply-Supervised Nets
This paper, published in 2015, received 526 indexed citations . Written by Chen‐Yu Lee, Saining Xie, Patrick W. Gallagher, Zhengyou Zhang and Zhuowen Tu covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (386 citations), Artificial Intelligence (209 citations) and Radiology, Nuclear Medicine and Imaging (92 citations).
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.
This paper is also available at doi.org/w34701519.