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Detecting Brittle Decisions for Free: Leveraging Margin Consistency in
  Deep Robust Classifiers

Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers

26 June 2024
Jonas Ngnawé
Sabyasachi Sahoo
Y. Pequignot
Frédéric Precioso
Christian Gagné
    AAML
ArXivPDFHTML

Papers citing "Detecting Brittle Decisions for Free: Leveraging Margin Consistency in Deep Robust Classifiers"

4 / 4 papers shown
Title
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection
Bo Peng
Yadan Luo
Yonggang Zhang
Yixuan Li
Zhen Fang
24
12
0
27 Feb 2024
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
217
674
0
19 Oct 2020
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
222
1,832
0
03 Feb 2017
Safety Verification of Deep Neural Networks
Safety Verification of Deep Neural Networks
Xiaowei Huang
M. Kwiatkowska
Sen Wang
Min Wu
AAML
178
929
0
21 Oct 2016
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