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The Clever Hans Effect in Anomaly Detection

The Clever Hans Effect in Anomaly Detection

18 June 2020
Jacob R. Kauffmann
Lukas Ruff
G. Montavon
Klaus-Robert Muller
    AAML
ArXivPDFHTML

Papers citing "The Clever Hans Effect in Anomaly Detection"

7 / 7 papers shown
Title
Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?
Can Neural Decompilation Assist Vulnerability Prediction on Binary Code?
D. Cotroneo
F. C. Grasso
R. Natella
V. Orbinato
121
0
0
10 Dec 2024
Attribute Restoration Framework for Anomaly Detection
Attribute Restoration Framework for Anomaly Detection
Chaoqin Huang
Fei Ye
Jinkun Cao
Maosen Li
Ya Zhang
Cewu Lu
93
50
0
25 Nov 2019
Deep Semi-Supervised Anomaly Detection
Deep Semi-Supervised Anomaly Detection
Lukas Ruff
Robert A. Vandermeulen
Nico Görnitz
Alexander Binder
Emmanuel Müller
K. Müller
Marius Kloft
UQCV
54
545
0
06 Jun 2019
MNIST-C: A Robustness Benchmark for Computer Vision
MNIST-C: A Robustness Benchmark for Computer Vision
Norman Mu
Justin Gilmer
52
210
0
05 Jun 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really
  Learn
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin
S. Wäldchen
Alexander Binder
G. Montavon
Wojciech Samek
K. Müller
84
1,009
0
26 Feb 2019
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Stanislav Pidhorskyi
Ranya Almohsen
Donald Adjeroh
Gianfranco Doretto
UQCV
40
321
0
06 Jul 2018
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class
  Models
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
Jacob R. Kauffmann
K. Müller
G. Montavon
DRL
69
96
0
16 May 2018
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