Fetching the paper…
Reading the bibliography…
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness.
Visualization of neural networks using saliency maps
Niels J. S. Mørch, Ulrik Kjems, Lars Kai Hansen, Claus Svarer, Ian Law, Benny Lautrup, Stephen C. Strother, and Kelly Rehm · 1995
Earlier work this paper cites.
IROF: a low resource evaluation metric for explanation methods
Laura Rieger and Lars Kai Hansen · 2003
Earlier work this paper cites.
On quantitative aspects of model interpretability
An-phi Nguyen and María Rodríguez Martínez · 2007
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.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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.
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
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems, 2016
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Gregory S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian J. Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Józefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Gordon Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul A. Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda B. Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
Earlier work this paper cites.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim · 2018
Earlier work this paper cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
Earlier work this paper cites.
Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
Earlier work this paper cites.
Evaluating explanations by cognitive value
Ajay Chander and Ramya Srinivasan · 2018
Earlier work this paper cites.
Metrics for explainable AI: challenges and prospects
Robert R. Hoffman, Shane T. Mueller, Gary Klein, and Jordan Litman · 2018
Earlier work this paper cites.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
Cited alongside, same era.
Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
Cited alongside, same era.
innvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, and Pieter-Jan Kindermans · 2019
Cited alongside, same era.
One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques, 2019
Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, and Yunfeng Zhang · 2019
Cited alongside, same era.
Understanding deep networks via extremal perturbations and smooth masks, 2019
Ruth Fong, Mandela Patrick, and Andrea Vedaldi · 2019
Cited alongside, same era.
Measuring the quality of explanations: The system causability scale (SCS)
Andreas Holzinger, André M. Carrington, and Heimo Müller · 2020
Later among the works it cites.
Towards best practice in explaining neural network decisions with LRP
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin · 2020
Later among the works it cites.
Captum: A unified and generic model interpretability library for pytorch, 2020
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson · 2020
Later among the works it cites.
When explanations lie: Why many modified BP attributions fail
Leon Sixt, Maximilian Granz, and Tim Landgraf · 2020
Later among the works it cites.
Software for dataset-wide xai: From local explanations to global insights with zennit, corelay, and virelay, 2021
Christopher J. Anders, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Designing theory-driven user-centric explainable AI
Danding Wang, Qian Yang, Ashraf M. Abdul, and Brian Y. Lim · 2019
Cited alongside, same era.
Benchmarking Attribution Methods with Relative Feature Importance
Mengjiao Yang and Been Kim · 2019
Cited alongside, same era.
On the (in)fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, and Pradeep Ravikumar · 2019
Cited alongside, same era.
Debugging tests for model explanations
Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim · 2020
Cited alongside, same era.
Focus! rating xai methods and finding biases
Anna Arias-Duart, Ferran Parés, Dario Garcia-Gasulla, and Victor Gimenez-Abalos · 2021
Later among the works it cites.
dalex: Responsible machine learning with interactive explainability and fairness in python
Hubert Baniecki, Wojciech Kretowicz, Piotr Piatyszek, Jakub Wisniewski, and Przemyslaw Biecek · 2021
Later among the works it cites.
Evaluating saliency methods on artificial data with different background types
Céline Budding, Fabian Eitel, Kerstin Ritter, and Stefan Haufe · 2021
Later among the works it cites.
The out-of-distribution problem in explainability and search methods for feature importance explanations
Peter Hase, Harry Xie, and Mohit Bansal · 2021
Later among the works it cites.
Alibi explain: Algorithms for explaining machine learning models
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, and Alexandru Coca · 2021
Later among the works it cites.
Better metrics for evaluating explainable artificial intelligence
Avi Rosenfeld · 2021
Later among the works it cites.
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
Later among the works it cites.
Revisiting sanity checks for saliency maps
Gal Yona and Daniel Greenfeld · 2021
Later among the works it cites.
Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations
Leila Arras, Ahmed Osman, and Wojciech Samek · 2022
Closest in time.
Framework for evaluating faithfulness of local explanations
Sanjoy Dasgupta, Nave Frost, and Michal Moshkovitz · 2022
Closest in time.
Evaluating feature attribution: An information-theoretic perspective
Yao Rong, Tobias Leemann, Vadim Borisov, Gjergji Kasneci, and Enkelejda Kasneci · 2022
Closest in time.
Interpretable semantic photo geolocation
Jonas Theiner, Eric Müller-Budack, and Ralph Ewerth · 2022
Closest in time.