Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks

K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps,” arXiv preprint arXiv:1312.6034

Original

2013

Earlier work this paper cites.

M. D. Zeiler and R. Fergus, “Visualizing and Understanding Convolutional Networks,” in European Conference on Computer Vision

2014

Earlier work this paper cites.

J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, “Striving for Simplicity: The All Convolutional Net,” arXiv preprint arXiv:1412.6806

Original

2014

Earlier work this paper cites.

S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation,” PloS one

2015

Earlier work this paper cites.

W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Müller, “Evaluating the Visualization of What a Deep Neural Network Has Learned,” IEEE Transactions on Neural Networks and Learning Systems

2016

Earlier work this paper cites.

B. Goodman and S. Flaxman, “European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation”,” AI magazine

2017

Earlier work this paper cites.

D. Doran, S. Schulz, and T. R. Besold, “What does explainable ai really mean? a new conceptualization of perspectives,” arXiv preprint arXiv:1710.00794

Original

2017

Earlier work this paper cites.

D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg, “Smoothgrad: Removing Noise by Adding Noise,” arXiv preprint arXiv:1706.03825

Original

2017

Earlier work this paper cites.

M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic Attribution for Deep Networks,” in International Conference on Machine Learning

2017

Earlier work this paper cites.

A. Shrikumar, P. Greenside, and A. Kundaje, “Learning Important Features Through Propagating Activation Differences,” in International Conference on Machine Learning

2017

Earlier work this paper cites.

G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller, “Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition,” Pattern Recognition

2017

Earlier work this paper cites.

R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization,” in IEEE International Conference on Computer Vision

2017

Earlier work this paper cites.

A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks,” in International Conference on Learning Representations

2018

Earlier work this paper cites.

J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim, “Sanity Checks for Saliency Maps,” Advances in Neural Information Processing Systems

2018

Earlier work this paper cites.

D. Alvarez-Melis and T. S. Jaakkola, “On the Robustness of Interpretability Methods,” arXiv preprint arXiv:1806.08049

Original

2018

Cited alongside, same era.

W. Nie, Y. Zhang, and A. Patel, “A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations,” in International Conference on Machine Learning

2018

Cited alongside, same era.

A. Ross and F. Doshi-Velez, “Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients,” in Proceedings of the AAAI Conference on Artificial Intelligence

2018

Cited alongside, same era.

C. Rudin, “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead,” Nature Machine Intelligence

2019

Cited alongside, same era.

A.-K. Dombrowski, M. Alber, C. Anders, M. Ackermann, K.-R. Müller, and P. Kessel, “Explanations Can be Manipulated and Geometry is to Blame,” in Advances in Neural Information Processing Systems

2019

Later among the works it cites.

C. Etmann, S. Lunz, P. Maass, and C. Schoenlieb, “On the Connection Between Adversarial Robustness and Saliency Map Interpretability,” in International Conference on Machine Learning

2019

Later among the works it cites.

B. Kim, J. Seo, and T. Jeon, “Bridging Adversarial Robustness and Gradient Interpretability,” arXiv preprint arXiv:1903.11626

Original

2019

Later among the works it cites.

A. Ignatiev, N. Narodytska, and J. Marques-Silva, “On Relating Explanations and Adversarial Examples,” Advances in Neural Information Processing Systems

2019

Later among the works it cites.