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Saliency strikes back: How filtering out high frequencies improves
  white-box explanations

Saliency strikes back: How filtering out high frequencies improves white-box explanations

18 July 2023
Sabine Muzellec
Thomas Fel
Victor Boutin
Léo Andéol
R. V. Rullen
Thomas Serre
    FAtt
ArXivPDFHTML

Papers citing "Saliency strikes back: How filtering out high frequencies improves white-box explanations"

5 / 5 papers shown
Title
HIVE: Evaluating the Human Interpretability of Visual Explanations
HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim
Nicole Meister
V. V. Ramaswamy
Ruth C. Fong
Olga Russakovsky
66
114
0
06 Dec 2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based
  Sensitivity Analysis
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
Thomas Fel
Rémi Cadène
Mathieu Chalvidal
Matthieu Cord
David Vigouroux
Thomas Serre
MLAU
FAtt
AAML
114
58
0
07 Nov 2021
Investigating Saturation Effects in Integrated Gradients
Investigating Saturation Effects in Integrated Gradients
Vivek Miglani
Narine Kokhlikyan
B. Alsallakh
Miguel Martin
Orion Reblitz-Richardson
FAtt
21
23
0
23 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and
  Goals of Human Trust in AI
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
Alon Jacovi
Ana Marasović
Tim Miller
Yoav Goldberg
252
426
0
15 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
251
3,684
0
28 Feb 2017
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