Fetching the paper…
Reading the bibliography…
We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks.
Gene selection for cancer classification using support vector machines
Isabelle Guyon, Jason Weston, Stephen Barnhill, and Vladimir Vapnik · 2002
Earlier work this paper cites.
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.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
Earlier work this paper cites.
Do deep nets really need to be deep?
Lei Jimmy Ba and Rich Caruana · 2014
Earlier work this paper cites.
Birdsnap: Large-scale fine-grained visual categorization of birds
Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L. Alexander, David W. Jacobs, and Peter N. Belhumeur · 2014
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
An isotropic 3x3 image gradient operator
Irwin Sobel · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Object Detectors Emerge in Deep Scene CNNs
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, January 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
Earlier work this paper cites.
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
A. Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje · 2016
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for Deep Neural Networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2017
Cited alongside, same era.
Real Time Image Saliency for Black Box Classifiers
P. Dabkowski and Y. Gal · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C. Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Distilling a Neural Network Into a Soft Decision Tree
N. Frosst and G. Hinton · 2017
Cited alongside, same era.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
Learning Important Features Through Propagating Activation Differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Later among the works it cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Later among the works it cites.
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
M. Wu, M. C. Hughes, S. Parbhoo, M. Zazzi, V. Roth, and F. Doshi-Velez · 2017
Later among the works it cites.
Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Training and investigating Residual Nets
S. Gross and M. Wilber · 2017
Cited alongside, same era.
The (Un)reliability of saliency methods
P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Schütt, S. Dähne, D. Erhan, and B. Kim · 2017
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
Cited alongside, same era.
SVCCA: singular vector canonical correlation analysis for deep learning dynamics and interpretability
Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Andrew Slavin Ross and Finale Doshi-Velez · 2017
Cited alongside, same era.
Local explanation methods for deep neural networks lack sensitivity to parameter values
Julius Adebayo, Justin Gilmer, Ian Goodfellow, and Been Kim · 2018
Closest in time.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory sayres · 2018
Closest in time.
Learning how to explain neural networks: Patternnet and patternattribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne · 2018
Closest in time.
Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, and Quoc V. Le · 2018
Closest in time.
Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, and Finale Doshi-Velez · 2018
Closest in time.
On the importance of single directions for generalization
A. S. Morcos, D. G. T. Barrett, N. C. Rabinowitz, and M. Botvinick · 2018
Closest in time.
RISE: randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Closest in time.
Revisiting the importance of individual units in cnns via ablation
Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba · 2018
Closest in time.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2019
Closest in time.