Deep Residual Learning for Image Recognition
- Authors
- Kaiming HeXiangyu ZhangShaoqing RenJian Sun
- Journal
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
In The Last Decade
doi.org/10.1109/cvpr.2016.90 →Countries where authors are citing Deep Residual Learning for Image Recognition
This map shows the geographic impact of Deep Residual Learning for Image Recognition. 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 Deep Residual Learning for Image Recognition with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Residual Learning for Image Recognition more than expected).
Fields of papers citing Deep Residual Learning for Image Recognition
This network shows the impact of Deep Residual Learning for Image Recognition. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep Residual Learning for Image Recognition.
About Deep Residual Learning for Image Recognition
This paper, published in 2016, received 132.1k indexed citations . Written by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun 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 (70.4k citations), Artificial Intelligence (44.0k citations) and Radiology, Nuclear Medicine and Imaging (13.7k citations). Published in LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas).
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/10.1109/cvpr.2016.90.