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The advancements in deep learning-based methods for visual perception tasks have seen astounding growth in the last decade, with widespread adoption in a plethora of application areas from autonomous driving to clinical decision support systems.
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Marko Robnik-Šikonja and Igor Kononenko · 2008
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Rajat Raina, Anand Madhavan, and Andrew Y Ng · 2009
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John Nickolls and William J Dally · 2010
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Matthew D Zeiler, Graham W Taylor, and Rob Fergus · 2011
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Are we ready for autonomous driving? the KITTI vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Intelligible models for classification and regression
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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Deep inside convolutional networks: Visualizing image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Diederik P Kingma and Jimmy Ba · 2014
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Karen Simonyan and Andrew Zisserman · 2014
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Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Matthew D Zeiler and Rob Fergus · 2014
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Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Jürgen Schmidhuber · 2015
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European Union regulations on algorithmic decision-making and a “right to explanation”
Bryce Goodman and Seth Flaxman · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C. Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C. Nelson, Jessica L. Mega, and Dale R. Webster · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Visual intelligence: Sharpen your perception, change your life
Amy E Herman · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
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Metrics for explainable AI: Challenges and prospects
Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman · 2018
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The mythos of model interpretability
Zachary C Lipton · 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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Visual interpretability for deep learning: A survey
Quan-shi Zhang and Song-Chun Zhu · 2018
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Gradient-based attribution methods
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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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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Interpretability of deep learning models: A survey of results
Supriyo Chakraborty, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani Srivastava, Alun Preece, Simon Julier, Raghuveer M Rao, et al · 2017
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Human attention in visual question answering: Do humans and deep networks look at the same regions?
Abhishek Das, Harsh Agrawal, Larry Zitnick, Devi Parikh, and Dhruv Batra · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2019
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Saliency prediction in the deep learning era: Successes and limitations
Ali Borji · 2019
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Machine learning interpretability: A survey on methods and metrics
Diogo V. Carvalho, Eduardo M. Pereira, and Jaime S. Cardoso · 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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Techniques for interpretable machine learning
Mengnan Du, Ninghao Liu, and Xia Hu · 2019
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Understanding deep networks via extremal perturbations and smooth masks
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 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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The (un)reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 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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Suraj Srinivas and François Fleuret · 2019
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CDeepEx: Contrastive deep explanations
Amir Feghahati, Christian R Shelton, Michael J Pazzani, and Kevin Tang · 2020
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A survey of deep learning techniques for autonomous driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, and Gigel Macesanu · 2020
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SmoothGrad with PyTorch
Kazuto Nakashima · 2020
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Convolutional neural network visualizations
Utku Ozbulak · 2020
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Contrastive explanations in neural networks
Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, and Ghassan AlRegib · 2020
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Explainable deep learning models in medical image analysis
Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan · 2020
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Score-CAM: Score-weighted visual explanations for convolutional neural networks
Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, and Xia Hu · 2020
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