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A plethora of methods have been proposed to explain how deep neural networks reach their decisions but comparatively, little effort has been made to ensure that the explanations produced by these methods are objectively relevant.
The proof and measurement of association between two things
Charles Spearman · 1904
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Lee R. Dice · 1945
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Olivier Bousquet and André Elisseeff · 2002
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Distance-based classification with lipschitz functions
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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A survey of explanations in recommender systems
Nava Tintarev and Judith Masthoff · 2007
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Reading tea leaves: How humans interpret topic models
Jonathan Chang, Sean Gerrish, Chong Wang, Jordan L. Boyd-graber, and David M. Blei · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Adaptive deconvolutional networks for mid and high level feature learning
M. D. Zeiler, G. W. Taylor, and R. Fergus · 2011
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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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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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, and Klaus-Robert Müller · 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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Understanding neural networks through representation erasure, 2016
Jiwei Li, Will Monroe, and Dan Jurafsky · 2016
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The mythos of model interpretability
Zachary C. Lipton · 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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Object detectors emerge in deep scene cnns
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J. Foster, and Matus Telgarsky · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Pot python optimal transport library, 2017
Rémi Flamary and Nicolas Courty · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C. Fong and Andrea Vedaldi · 2017
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
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A unified approach to interpreting model predictions
Scott Lundberg and Su-In Lee · 2017
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Exploring generalization in deep learning
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 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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An evaluation of the human-interpretability of explanation
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, and Finale Doshi-Velez · 2019
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Learning what and where to attend
Drew Linsley, Dan Shiebler, Sven Eberhardt, and Thomas Serre · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
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Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 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, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 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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Kevin Scaman and Aladin Virmaux · 2019
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Quantifying interpretability and trust in machine learning systems
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Computing linear restrictions of neural networks
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Sanity checks for saliency metrics
Richard Tomsett, Dan Harborne, Supriyo Chakraborty, Prudhvi Gurram, and Alun Preece · 2019
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On the (in)fidelity and sensitivity for explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, and Pradeep Ravikumar · 2019
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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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Training robust neural networks using lipschitz bounds, 2020
Patricia Pauli, Anne Koch, Julian Berberich, and Frank Allgöwer · 2020
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Achieving robustness in classification using optimal transport with hinge regularization, 2020
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When explanations lie: Why many modified bp attributions fail
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Visualizing the impact of feature attribution baselines
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The many faces of 1-lipschitz neural networks, 2021
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Coalitional strategies for efficient individual prediction explanation
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