Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Impact in
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- arXiv (Cornell University)
In The Last Decade
doi.org/w5863785 →Countries where authors are citing Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
This map shows the geographic impact of Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. 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 Discriminative Unsupervised Feature Learning with Convolutional Neural Networks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Discriminative Unsupervised Feature Learning with Convolutional Neural Networks more than expected).
Fields of papers citing Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
This network shows the impact of Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Discriminative Unsupervised Feature Learning with Convolutional Neural Networks.
About Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
This paper, published in 2014, received 289 indexed citations . Written by Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller and Thomas Brox 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 (197 citations), Artificial Intelligence (162 citations), Radiology, Nuclear Medicine and Imaging (23 citations), Media Technology (23 citations) and Biomedical Engineering (21 citations). Published in arXiv (Cornell University).
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/w5863785.