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Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample.
Fast exact multiplication by the hessian
Barak A. Pearlmutter · 1994
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Spearman correlation coefficients, differences between
Leann Myers and Maria J Sirois · 2004
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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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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The pascal visual object classes challenge: A retrospective
Mark Everingham, SM Ali Eslami, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 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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Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne · 2016
Cited alongside, same era.
Salient deconvolutional networks
A. Mahendran and A. Vedaldi · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Top-down neural attention by excitation backprop
Jianming Zhang, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Real time image saliency for black box classifiers
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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A-fast-rcnn: Hard positive generation via adversary for object detection
Xiaolong Wang, Abhinav Shrivastava, and Abhinav Gupta · 2017
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Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Evaluating feature importance estimates
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Distilling a neural network into a soft decision tree
Nicholas Frosst and Geoffrey Hinton · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Grad-CAM: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
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Discriminability objective for training descriptive captions
Ruotian Luo, Brian Price, Scott Cohen, and Gregory Shakhnarovich · 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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Understanding deep networks via extremal perturbations and smooth masks
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 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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Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks
Jose Oramas, Kaili Wang, and Tinne Tuytelaars · 2019
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Benchmarking attribution methods with relative feature importance
Mengjiao Yang and Been Kim · 2019
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