Recently many methods have been introduced to explain CNN decisions.
Reading the bibliography…
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.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
CNN features off-the-shelf: An astounding baseline for recognition
Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson · 2014
Earlier work this paper cites.
Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana · 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.
Object Detectors Emerge in Deep Scene CNNs
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K Simonyan and A Zisserman · 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 Bernstein, and others · 2015
Earlier work this paper cites.
Tensorflow: Large-scale machine learning on heterogeneous systems, 2015
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2015
Earlier work this paper cites.
Data-free Parameter Pruning for Deep Neural Networks
Suraj Srinivas and R Venkatesh Babu · 2015
Earlier work this paper cites.
”why should i trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Learning Deep Features for Discriminative Localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Earlier work this paper cites.
Towards the Science of Security and Privacy in Machine Learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman · 2016
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
Cited alongside, same era.
Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
Learning Important Features Through Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
SmoothGrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Object-Extent Pooling for Weakly Supervised Single-Shot Localization
Marco Loog Amogh Gudi Nicolai van Rosmalen and Jan van Gemert · 2017
Cited alongside, same era.
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C. Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
Cited alongside, same era.
A unified view of gradient-based attribution methods for Deep Neural Networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
Cited alongside, same era.
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 · 2017
Later among the works it cites.
Interpretation of Neural Networks is Fragile
Amirata Ghorbani, Abubakar Abid, and James Zou · 2017
Later among the works it cites.
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Later among the works it cites.
A Survey of Model Compression and Acceleration for Deep Neural Networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2017
Later among the works it cites.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Amina Adadi and Mohammed Berrada · 2018
Later among the works it cites.
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.
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.
Taking Human out of Learning Applications: A Survey on Automated Machine Learning
Quanming Yao, Mengshuo Wang, Yuqiang Chen, Wenyuan Dai, Hu Yi-Qi, Li Yu-Feng, Tu Wei-Wei, Yang Qiang, and Yu Yang · 2018
Later among the works it cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
Later among the works it cites.
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks
Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, and Hanqing Lu · 2018
Later among the works it cites.
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
Bolun Wang, Yao Yuanshun, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao · 2019
Closest in time.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2019
Closest in time.
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