Feature Robustness Evaluation
Feature Robustness Evaluation is a methodological framework within feature stability analysis that systematically assesses the sensitivity and consistency of feature representations under perturbations, thereby distinguishing it by its focus on resilience to variations in input conditions or model configurations.
55
Publications
3.3K
Citations
259
Authors
114
Institutions
Publications per year
2017–2026
41
Parents
55
Multi-cue Correlation Filters for Robust Visual Tracking
Ning Wang, Wengang Zhou, Qi Tian et al. · 2018 · 488 citations
Adversarial Examples Are Not Bugs, They Are Features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras et al. · arXiv (Cornell University) · 2019 · 395 citations · Full text
Assessing robustness of radiomic features by image perturbation
Scientific Reports · 2019 · 243 citations · Full text
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University of Science and Technology of China
Hefei, China
4 h-index
8 publications
1.6K citations
Florida Atlantic University
Boca Raton, United States
3 h-index
8 publications
108 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
151 citations
Western Kentucky University
Bowling Green, United States
2 h-index
2 publications
28 citations
Cancer Research UK Cambridge Center
Cambridge, United Kingdom
2 h-index
7 publications
400 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
6 publications
87 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
25 publications
1.3K citations
Loughborough University
Loughborough, United Kingdom
1 h-index
2 publications
40 citations
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4 publications · 435 citations
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Repository · 4 publications · 477 citations
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1 publications · 36 citations
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1 publications · 18 citations
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BMT · 1 publications · 89 citations
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