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The evaluation of the fidelity of eXplainable Artificial Intelligence (XAI) methods to their underlying models is a challenging task, primarily due to the absence of a ground truth for explanations.
Using sensitivity analysis and visualization techniques to open black box data mining models
P. Cortez and M. J. Embrechts · 2012
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A benchmark of computational models of saliency to predict human fixations
T. Judd, F. Durand, and A. Torralba · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Saliency and human fixations: State-of-the-art and study of comparison metrics
N. Riche, M. Duvinage, M. Mancas, B. Gosselin, and T. Dutoit · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
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" why should i trust you?" explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Evaluating the Visualization of What a Deep Neural Network Has Learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Muller · 2016
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
A. Adadi and M. Berrada · 2018
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Towards Robust Interpretability with Self-Explaining Neural Networks
D. Alvarez-Melis and T. S. Jaakkola · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
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Methods for interpreting and understanding deep neural networks
Irof: a low resource evaluation metric for explanation methods
L. Rieger and L. K. Hansen · 2020
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Sanity checks for saliency metrics
R. Tomsett, D. Harborne, S. Chakraborty, P. Gurram, and A. Preece · 2020
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Score-cam: Score-weighted visual explanations for convolutional neural networks
H. Wang, Z. Wang, M. Du, F. Yang, Z. Zhang, S. Ding, P. Mardziel, and X. Hu · 2020
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Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations
L. Arras, A. Osman, and W. Samek · 2021
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Large image datasets: A pyrrhic win for computer vision?
A. Birhane and V. U. Prabhu · 2021
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On the receptive field misalignment in cam-based visual explanations
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G. Montavon, W. Samek, and K.-R. Müller · 2018
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RISE: Randomized Input Sampling for Explanation of Black-box Models
V. Petsiuk, A. Das, and K. Saenko · 2018
Cited alongside, same era.
One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
V. Arya, R. K. Bellamy, P.-Y. Chen, A. Dhurandhar, M. Hind, S. C. Hoffman, S. Houde, Q. V. Liao, R. Luss, A. Mojsilović, et al · 2019
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Testing the robustness of attribution methods for convolutional neural networks in mri-based alzheimer’s disease classification
F. Eitel, K. Ritter, and A. D. N. I. (ADNI) · 2019
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Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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On the (in) fidelity and sensitivity of explanations
C.-K. Yeh, C.-Y. Hsieh, A. Suggala, D. I. Inouye, and P. K. Ravikumar · 2019
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Evaluating and aggregating feature-based model explanations
U. Bhatt, A. Weller, and J. M. Moura · 2020
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P. Xia, H. Niu, Z. Li, and B. Li · 2021
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Metrics for saliency map evaluation of deep learning explanation methods
T. Gomez, T. Fréour, and H. Mouchère · 2022
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Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
A. Mamalakis, E. A. Barnes, and I. Ebert-Uphoff · 2022
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Evaluating explainable artificial intelligence for x-ray image analysis
M. Miró-Nicolau, G. Moyà-Alcover, and A. Jaume-i Capó · 2022
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Visual explanation of black-box model: similarity difference and uniqueness (sidu) method
S. M. Muddamsetty, M. N. Jahromi, A. E. Ciontos, L. M. Fenoy, and T. B. Moeslund · 2022
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Generating perturbation-based explanations with robustness to out-of-distribution data
L. Qiu, Y. Yang, C. C. Cao, Y. Zheng, H. Ngai, J. Hsiao, and L. Chen · 2022
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A consistent and efficient evaluation strategy for attribution methods
Y. Rong, T. Leemann, V. Borisov, G. Kasneci, and E. Kasneci · 2022
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Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
B. H. van der Velden, H. J. Kuijf, K. G. Gilhuijs, and M. A. Viergever · 2022
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A novel approach to generate datasets with xai ground truth to evaluate image models
M. Miró-Nicolau, A. Jaume-i Capó, and G. Moyà-Alcover · 2023
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