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One of the unsolved challenges in the field of Explainable AI (XAI) is determining how to most reliably estimate the quality of an explanation method in the absence of ground truth explanation labels.
Individual comparisons by ranking methods
Frank Wilcoxon · 1945
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Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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The mythos of model interpretability
Zachary C. Lipton · 2018
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
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Mukund Sundararajan and Ankur Taly · 2018
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Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
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Explanations can be manipulated and geometry is to blame
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Investigating sanity checks for saliency maps with image and text classification
Narine Kokhlikyan, Vivek Miglani, Bilal Alsallakh, Miguel Martin, and Orion Reblitz-Richardson · 2021
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Toward explainable AI for regression models
Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, and Grégoire Montavon · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Order in the court: Explainable AI methods prone to disagreement
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Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Benchmarking Attribution Methods with Relative Feature Importance
Mengjiao Yang and Been Kim · 2019
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On the (in)fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, and Pradeep Ravikumar · 2019
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SAM: the sensitivity of attribution methods to hyperparameters
Naman Bansal, Chirag Agarwal, and Anh Nguyen · 2020
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Evaluating and aggregating feature-based model explanations
Umang Bhatt, Adrian Weller, and José M. F. Moura · 2020
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Evaluating input perturbation methods for interpreting CNNs and saliency map comparison
Lukas Brunke, Prateek Agrawal, and Nikhil George · 2020
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Michael Neely, Stefan F. Schouten, Maurits J. R. Bleeker, and Ana Lucic · 2021
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Revisiting sanity checks for saliency maps
Gal Yona and Daniel Greenfeld · 2021
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CLEVR-XAI: A benchmark dataset for the ground truth evaluation of neural network explanations
Leila Arras, Ahmed Osman, and Wojciech Samek · 2022
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Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2022
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Evaluation of interpretability methods and perturbation artifacts in deep neural networks
Lennart Brocki and Neo Christopher Chung · 2022
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Noisegrad - enhancing explanations by introducing stochasticity to model weights
Kirill Bykov, Anna Hedström, Shinichi Nakajima, and Marina M.-C. Höhne · 2022
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Learning to estimate shapley values with vision transformers
Ian Covert, Chanwoo Kim, and Su-In Lee · 2022
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Framework for evaluating faithfulness of local explanations
Sanjoy Dasgupta, Nave Frost, and Michal Moshkovitz · 2022
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Evaluating feature attribution methods in the image domain
Arne Gevaert, Axel-Jan Rousseau, Thijs Becker, Dirk Valkenborg, Tijl De Bie, and Yvan Saeys · 2022
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The disagreement problem in explainable machine learning: A practitioner’s perspective
Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju · 2022
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Optimizing explanations by network canonization and hyperparameter search
Frederik Pahde, Galip Ümit Yolcu, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin · 2022
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A consistent and efficient evaluation strategy for attribution methods
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, and Enkelejda Kasneci · 2022
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Interpretable semantic photo geolocation
Jonas Theiner, Eric Müller-Budack, and Ralph Ewerth · 2022
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A unified study of machine learning explanation evaluation metrics
Yipei Wang and Xiaoqian Wang · 2022
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Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, and Marina M.-C. Höhne · 2023
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Do backpropagation trained neural networks have normal weight distributions?
I. Bellido and Emile Fiesler · 2063
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