Pseudo-labeling and confirmation bias in deep semi-supervised learning
- Journal
- Dublin City University Open Access Institutional Repository (Dublin City University)
In The Last Decade
doi.org/w2858311 →Countries where authors are citing Pseudo-labeling and confirmation bias in deep semi-supervised learning
This map shows the geographic impact of Pseudo-labeling and confirmation bias in deep semi-supervised learning. 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 Pseudo-labeling and confirmation bias in deep semi-supervised learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pseudo-labeling and confirmation bias in deep semi-supervised learning more than expected).
Fields of papers citing Pseudo-labeling and confirmation bias in deep semi-supervised learning
This network shows the impact of Pseudo-labeling and confirmation bias in deep semi-supervised learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Pseudo-labeling and confirmation bias in deep semi-supervised learning.
About Pseudo-labeling and confirmation bias in deep semi-supervised learning
This paper, published in 2020, received 418 indexed citations . Written by Eric Arazo, Diego Ortego, Paul Albert, Noel E. O’Connor and Kevin McGuinness covering the research area of Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (272 citations), Computer Vision and Pattern Recognition (205 citations), Radiology, Nuclear Medicine and Imaging (48 citations), Media Technology (26 citations) and Signal Processing (21 citations). Published in Dublin City University Open Access Institutional Repository (Dublin City 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/w2858311.