Unsupervised Outlier Detection
Unsupervised Outlier Detection is an outlier detection approach that identifies anomalies in data without relying on labeled instances, distinguishing itself from supervised and semi-supervised methods by operating solely on unlabeled datasets under the assumption that outliers deviate significantly from the majority pattern.
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489
Institutions
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2017–2026
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Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo · arXiv (Cornell University) · 2013 · 811 citations · Full text
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National University of Defense Technology
Changsha, China
9 h-index
32 publications
893 citations

University of Alberta
Edmonton, Canada
9 h-index
15 publications
4.6K citations
Ludwig-Maximilians-Universität München
Munich, Germany
6 h-index
15 publications
4.4K citations
Universidade de São Paulo
São Paulo, Brazil
6 h-index
9 publications
2.8K citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
12 publications
565 citations

The University of Melbourne
Melbourne, Australia
5 h-index
13 publications
922 citations

Beihang University
Beijing, China
5 h-index
12 publications
263 citations
University of Technology Sydney
Sydney, Australia
4 h-index
12 publications
1.1K citations
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Repository · 20 publications · 1.4K citations
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7 publications · 208 citations
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