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Counterfactual examples identify how inputs can be altered to change the predicted class of a classifier, thus opening up the black-box nature of, e.g., deep neural networks.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
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Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
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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
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NICE: Non-Linear Independent Components Estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Mai Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Causes of effects and effects of causes
Judea Pearl · 2015
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2015
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”Why should i trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus Robert Müller · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, and Max Welling · 2017
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Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin Yu Chen, Ronny Luss, Chun Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
Cited alongside, same era.
One pixel attack for fooling deep neural networks
Jiawei Su, Danilo Vasconcellos Vargas, and Kouichi Sakurai · 2019
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Interpretable counterfactual explanations guided by prototypes
Arnaud Van Looveren and Janis Klaise · 2019
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CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines
Arjun Akula, Shuai Wang, and Song-Chun Zhu · 2020
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Training normalizing flows with the information bottleneck for competitive generative classification
Lynton Ardizzone, Radek Mackowiak, Carsten Rother, and Ullrich Köthe · 2020
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ViCE: Visual Counterfactual Explanations for Machine Learning Models
Oscar Gomez, Steffen Holter, Jun Yuan, and Enrico Bertini · 2020
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Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky T.Q. Chen, David Duvenaud, and Jörn Henrik Jacobsen · 2019
Cited alongside, same era.
Explaining image classifiers by counterfactual generation
Chun Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2019
Cited alongside, same era.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2019
Cited alongside, same era.
Counterfactual Visual Explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
Cited alongside, same era.
InfoCNF: Efficient conditional continuous normalizing flow using adaptive solvers
Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, and Anima Anandkumar · 2019
Cited alongside, same era.
Question-conditioned counterfactual image generation for VQA
Jingjing Pan, Yash Goyal, and Stefan Lee · 2019
Cited alongside, same era.
Pavel Izmailov, Polina Kirichenko, Marc Finzi, and Andrew Gordon Wilson · 2020
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Counterfactual explanation based on gradual construction for deep networks
Sin-Han Kang, Honggyu Jung, Dong-Ok Won, and Seong-Whan Lee · 2020
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Generative classifiers as a basis for trustworthy computer vision
Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, and Carsten Rother · 2020
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Explainable image classification with evidence counterfactual
Tom Vermeire and David Martens · 2020
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SCOUT: Self-aware Discriminant Counterfactual Explanations
Pei Wang and Nuno Vasconcelos · 2020
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le · 2020
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DECE: decision explorer with counterfactual explanations for machine learning models
Furui Cheng, Yao Ming, and Huamin Qu · 2021
Closest in time.
Contrastive Explanations for Model Interpretability
Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, and Yoav Goldberg · 2021
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Polyjuice: Automated, General-purpose Counterfactual Generation
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S. Weld · 2021
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