Fetching the paper…
Reading the bibliography…
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also presents significant challenges.
A value for n-person games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Lèon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Polynomial calculation of the shapley value based on sampling
Javier Castro, Daniel Gómez, and Juan Tejada · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 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.
Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
Earlier work this paper cites.
Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Inverting visual representations with convolutional networks
Alexey Dosovitskiy and Thomas Brox · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Harnessing deep neural networks with logic rules
Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, and Eric P. Xing · 2016
Cited alongside, same era.
Learning deep parsimonious representations
Renjie Liao, Alex Schwing, Richard Zemel, and Raquel Urtasun · 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.
Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
Cited alongside, same era.
Tianfu Wu, Xilai Li, Xi Song, Wei Sun, Liang Dong, and Bo Li · 2017
Later among the works it cites.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Later among the works it cites.
Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S. Jaakkola · 2018
Later among the works it cites.
L-shapley and c-shapley: Efficient model interpretation for structured data
Jianbo Chen, Le Song, Martin J. Wainwright, and Michael I. Jordan · 2018
Later among the works it cites.
Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C. Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
β \beta -vae: learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
Cited alongside, same era.
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
PangWei Koh and Percy Liang · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
Cited alongside, same era.
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
Later among the works it cites.
Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
Later among the works it cites.
Evaluating recurrent neural network explanations
Leila Arras, Ahmed Osman, Klaus-Robert Müller, and Wojciech Samek · 2019
Closest in time.
Can i trust the explainer? verifying post-hoc explanatory methods
Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster, Thomas Lukasiewicz, and Phil Blunsom · 2019
Closest in time.
An integrative 3c evaluation framework for explainable artificial intelligence
Xiaocong Cui, Jung Min Lee, and J Hsieh · 2019
Closest in time.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2019
Closest in time.
Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks
Jose Oramas, Kaili Wang, and Tinne Tuytelaars · 2019
Closest in time.
Evaluating explainers via perturbation
Minh N Vu, Truc D Nguyen, NhatHai Phan, Ralucca Gera, and My T Thai · 2019
Closest in time.
Evaluating explanation without ground truth in interpretable machine learning
Fan Yang, Mengnan Du, and Xia Hu · 2019
Closest in time.
Bim: Towards quantitative evaluation of interpretability methods with ground truth
Mengjiao Yang and Been Kim · 2019
Closest in time.