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Saliency maps that identify the most informative regions of an image for a classifier are valuable for model interpretability.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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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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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 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 Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Towards a rigorous science of interpretable machine learning, 2017
Finale Doshi-Velez and Been Kim · 2017
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Adversarial localization network
Lijie Fan, Shengjia Zhao, and Stefano Ermon · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg · 2017
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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Later among the works it cites.
Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2018
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Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
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Explaining an image classifier’s decisions using generative models
Chirag Agarwal and Anh Nguyen · 2019
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Large-scale interactive object segmentation with human annotators
Rodrigo Benenson, Stefan Popov, and Vittorio Ferrari · 2019
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M. Zintgraf, Taco Cohen, Tameem Adel, and Max Welling · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
Cited alongside, same era.
Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2018
Cited alongside, same era.
Consistent individualized feature attribution for tree ensembles
Scott M. Lundberg, Gabriel G. Erion, and Su-In Lee · 2018
Cited alongside, same era.
Explaining image classifiers by counterfactual generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2019
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Deep fusion network for image completion
Xin Hong, Pengfei Xiong, Renhe Ji, and Haoqiang Fan · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2019
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BIM: towards quantitative evaluation of interpretability methods with ground truth
Mengjiao Yang and Been Kim · 2019
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Evaluating weakly supervised object localization methods right
Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim · 2020
Closest in time.
Classifier-agnostic saliency map extraction
Konrad Zolna, Krzysztof J. Geras, and Kyunghyun Cho · 2020
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