Invariant Representation Learning
Invariant Representation Learning is a representation learning paradigm that derives data embeddings which preserve task-relevant information while remaining unchanged under specified transformations or variations in the input space.
283
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19.9K
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786
Authors
281
Institutions
Publications per year
2017–2026
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Invariant visual representation by single neurons in the human brain
Rodrigo Quian Quiroga, Leila Reddy, Gabriel Kreiman et al. · Nature · 2005 · 2K citations · Full text
Understanding deep image representations by inverting them
Aravindh Mahendran, Andrea Vedaldi · 2015 · 1.9K citations
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Žbontar, Jing Li, Ishan Misra et al. · arXiv (Cornell University) · 2021 · 779 citations · Full text
Training Invariant Support Vector Machines
Dennis DeCoste, Bernhard Schölkopf · Machine Learning · 2002 · 582 citations · Full text
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University of Oxford
Oxford, United Kingdom
12 h-index
30 publications
5.5K citations

Chinese Academy of Sciences
Beijing, China
12 h-index
43 publications
3.3K citations
Massachusetts Institute of Technology
Cambridge, United States
11 h-index
29 publications
3.1K citations
University of California, Berkeley
Berkeley, United States
9 h-index
21 publications
1.2K citations
Stanford University
Stanford, United States
8 h-index
19 publications
2.3K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
14 publications
1.2K citations
Institute of Computing Technology
Beijing, China
6 h-index
29 publications
2.8K citations

University of Toronto
Toronto, Canada
6 h-index
11 publications
1.8K citations
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Repository · 83 publications · 5K citations
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6 publications · 161 citations
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Conference · 5 publications · 220 citations
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