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Plausible Counterfactuals: Auditing Deep Learning Classifiers with
  Realistic Adversarial Examples

Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples

25 March 2020
Alejandro Barredo Arrieta
Javier Del Ser
    AAML
ArXivPDFHTML

Papers citing "Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples"

5 / 5 papers shown
Title
Probabilistically Plausible Counterfactual Explanations with Normalizing
  Flows
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
Patryk Wielopolski
Oleksii Furman
Jerzy Stefanowski
Maciej Ziȩba
46
2
0
27 May 2024
Identifying Spurious Correlations using Counterfactual Alignment
Identifying Spurious Correlations using Counterfactual Alignment
Joseph Paul Cohen
Louis Blankemeier
Akshay S. Chaudhari
CML
57
1
0
01 Dec 2023
Discriminative Attribution from Counterfactuals
Discriminative Attribution from Counterfactuals
N. Eckstein
A. S. Bates
G. Jefferis
Jan Funke
FAtt
CML
27
1
0
28 Sep 2021
An analysis on the use of autoencoders for representation learning:
  fundamentals, learning task case studies, explainability and challenges
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
D. Charte
F. Charte
M. J. D. Jesus
Francisco Herrera
SSL
OOD
27
51
0
21 May 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
1