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While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields new challenges in generating trustworthy explanations, since the established rule sets might not be sufficient or applicable to novel network structures.
“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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“Deep inside convolutional networks: Visualising image classification models and saliency maps,”
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, · 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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“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
Sergey Ioffe and Christian Szegedy, · 2015
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“Very deep convolutional networks for large-scale image recognition,”
Karen Simonyan and Andrew Zisserman, · 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 S. Bernstein, Alexander C. Berg, and Li Fei-Fei, · 2015
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“Striving for simplicity: The all convolutional net,”
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller, · 2015
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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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“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
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“Not just a black box: Learning important features through propagating activation differences,”
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje, · 2016
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“Learning important features through propagating activation differences,”
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje, · 2017
Cited alongside, same era.
“Axiomatic attribution for deep networks,”
Mukund Sundararajan, Ankur Taly, and Qiqi Yan, · 2017
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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, · 2017
Cited alongside, same era.
“Towards best practice in explaining neural network decisions with lrp,”
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin, · 2020
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Mathilde Guillemot, Catherine Heusele, Rodolphe Korichi, Sylvianne Schnebert, and Liming Chen, · 2020
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“Notes on canonization for resnets and densenets,” https://github.com/AlexBinder/LRP_Pytorch_Resnets_ Densenet/blob/master/canonization_doc.pdf, 2020
Alexander Binder, · 2020
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“Explaining deep neural networks and beyond: A review of methods and applications,”
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J Anders, and Klaus-Robert Müller, · 2021
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“Pruning by explaining: A novel criterion for deep neural network pruning,”
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Alexander Binder, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek, · 2021
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Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff, · 2018
Cited alongside, same era.
“Gradient-based vs. propagation-based explanations: An axiomatic comparison,”
Grégoire Montavon, · 2019
Cited alongside, same era.
“Batchnorm decomposition for deep neural network interpretation,”
Lucas Y. W. Hui and Alexander Binder, · 2019
Cited alongside, same era.
Later among the works it cites.
Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin, · 2021
Later among the works it cites.
“Visualization of neural networks using saliency maps,”
Niels JS Morch, Ulrik Kjems, Lars Kai Hansen, Claus Svarer, Ian Law, Benny Lautrup, Steve Strother, and Kelly Rehm, · 2090
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