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Feature removal is a central building block for eXplainable AI (XAI), both for occlusion-based explanations (Shapley values) as well as their evaluation (pixel flipping, PF).
A value for n-person games
Lloyd S Shapley · 1953
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Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
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An image inpainting technique based on the fast marching method
Alexandru Telea · 2004
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Explaining classifications for individual instances
M. Robnik-Sikonja and I. Kononenko · 2008
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The elements of statistical learning: data mining, inference, and prediction , volume 2
Trevor Hastie, Robert Tibshirani, and Jerome H Friedman · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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An efficient explanation of individual classifications using game theory
Erik Štrumbelj and Igor Kononenko · 2010
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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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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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Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation
Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin · 2015
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No-reference image quality assessment algorithms: A survey
Vipin Kamble and KM Bhurchandi · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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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 · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 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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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Explain black-box image classifications using superpixel-based interpretation
Yi Wei, Ming-Ching Chang, Yiming Ying, Ser Nam Lim, and Siwei Lyu · 2018
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Explaining image classifiers by counterfactual generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2019
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Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Causability and explainability of artificial intelligence in medicine
Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Müller · 2019
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Giles Hooker and Lucas Mentch · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Xrai: Better attributions through regions
Andrei Kapishnikov, Tolga Bolukbasi, Fernanda Viégas, and Michael Terry · 2019
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Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Explainable AI: interpreting, explaining and visualizing deep learning , volume 11700
Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, and Klaus-Robert Müller · 2019
Cited alongside, same era.
Benchmarking attribution methods with relative feature importance
Mengjiao Yang and Been Kim · 2019
Cited alongside, same era.
On the (in) fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar · 2019
Cited alongside, same era.
Explaining image classifiers by removing input features using generative models
Chirag Agarwal and Anh Nguyen · 2020
Cited alongside, same era.
Fairwashing explanations with off-manifold detergent
Christopher Anders, Plamen Pasliev, Ann-Kathrin Dombrowski, Klaus-Robert Müller, and Pan Kessel · 2020
Cited alongside, same era.
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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Metrics for saliency map evaluation of deep learning explanation methods
Tristan Gomez, Thomas Fréour, and Harold Mouchère · 2022
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Missingness bias in model debugging
Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, and Aleksander Madry · 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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Toward explainable artificial intelligence for regression models: A methodological perspective
Simon Letzgus, Patrick Wagner, Jonas Lederer, Wojciech Samek, Klaus-Robert Müller, and Gregoire Montavon · 2022
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Visualizing the effects of predictor variables in black box supervised learning models
Daniel W Apley and Jingyu Zhu · 2020
Cited alongside, same era.
Evaluating and aggregating feature-based model explanations
Umang Bhatt, Adrian Weller, and José MF Moura · 2020
Cited alongside, same era.
Towards novel insights in lattice field theory with explainable machine learning
Stefan Blücher, Lukas Kades, Jan M. Pawlowski, Nils Strodthoff, and Julian M. Urban · 2020
Cited alongside, same era.
A baseline for shapley values in mlps: From missingness to neutrality
Cosimo Izzo, Aldo Lipani, Ramin Okhrati, and Francesca Medda · 2020
Cited alongside, same era.
Explaining explanations: Axiomatic feature interactions for deep networks
Joseph D. Janizek, Pascal Sturmfels, and Su-In Lee · 2020
Cited alongside, same era.
Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, et al · 2020
Cited alongside, same era.
Problems with shapley-value-based explanations as feature importance measures
I Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler · 2020
Cited alongside, same era.
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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Antonios Mamalakis, Elizabeth A Barnes, and Imme Ebert-Uphoff · 2022
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Using shapley values and variational autoencoders to explain predictive models with dependent mixed features
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, and Kjersti Aas · 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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A unified survey on anomaly, novelty, open-set, and out of-distribution detection: Solutions and future challenges
Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2022
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Output-targeted baseline for neuron attribution calculation
Rui Shi, Tianxing Li, and Yasushi Yamaguchi · 2022
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Limesegment: Meaningful, realistic time series explanations
Torty Sivill and Peter Flach · 2022
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From attribution maps to human-understandable explanations through concept relevance propagation
Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek, and Sebastian Lapuschkin · 2023
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Analyzing and explaining image classifiers via diffusion guidance
Maximilian Augustin, Yannic Neuhaus, and Matthias Hein · 2023
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Shortcomings of top-down randomization-based sanity checks for evaluations of deep neural network explanations
Alexander Binder, Leander Weber, Sebastian Lapuschkin, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek · 2023
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Feature perturbation augmentation for reliable evaluation of importance estimators in neural networks
Lennart Brocki and Neo Christopher Chung · 2023
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Faithful to whom? questioning interpretability measures in nlp
Evan Crothers, Herna Viktor, and Nathalie Japkowicz · 2023
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Diffeomorphic counterfactuals with generative models
Ann-Kathrin Dombrowski, Jan E. Gerken, Klaus-Robert Müller, and Pan Kessel · 2023
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Don’t lie to me! robust and efficient explainability with verified perturbation analysis
Thomas Fel, Mélanie Ducoffe, David Vigouroux, Rémi Cadène, Mikael Capelle, Claire Nicodème, and Thomas Serre · 2023
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Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research
Timo Freiesleben and Gunnar König · 2023
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Out-of-distribution detection is not all you need
Joris Guérin, Kevin Delmas, Raul Ferreira, and Jérémie Guiochet · 2023
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Deletion and insertion tests in regression models
Naofumi Hama, Masayoshi Mase, and Art B Owen · 2023
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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 Marina M.-C. Höhne · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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ℳ 4 \mathcal{M}^{4} : A unified xai benchmark for faithfulness evaluation of feature attribution methods across metrics, modalities and models
Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, Himabindu Lakkaraju, and Haoyi Xiong · 2023
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Gradient strikes back: How filtering out high frequencies improves explanations
Sabine Muzellec, Leo Andeol, Thomas Fel, Rufin VanRullen, and Thomas Serre · 2023
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From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert · 2023
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Can we faithfully represent absence states to compute shapley values on a DNN?
Jie Ren, Zhanpeng Zhou, Qirui Chen, and Quanshi Zhang · 2023
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Manifold restricted interventional shapley values
Muhammad Faaiz Taufiq, Patrick Blöbaum, and Lenon Minorics · 2023
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Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
Johanna Vielhaben, Stefan Bluecher, and Nils Strodthoff · 2023
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Theoretical behavior of xai methods in the presence of suppressor variables
Rick Wilming, Leo Kieslich, Benedict Clark, and Stefan Haufe · 2023
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Inpaint anything: Segment anything meets image inpainting
Tao Yu, Runseng Feng, Ruoyu Feng, Jinming Liu, Xin Jin, Wenjun Zeng, and Zhibo Chen · 2023
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Toward explainable artificial intelligence for precision pathology
Frederick Klauschen, Jonas Dippel, Philipp Keyl, Philipp Jurmeister, Michael Bockmayr, Andreas Mock, Oliver Buchstab, Maximilian Alber, Lukas Ruff, Grégoire Montavon, et al · 2024
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