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Existing uncertainty modeling approaches try to detect an out-of-distribution point from the in-distribution dataset.
Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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The distribution of robust distances
Johanna Hardin and David M Rocke · 2005
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Classification with kernel mahalanobis distance classifiers
Bernard Haasdonk and Elżbieta Pękalska · 2009
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Handbook of image and video processing
Alan C Bovik · 2010
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Adaptive deconvolutional networks for mid and high level feature learning
Matthew D Zeiler, Graham W Taylor, and Rob Fergus · 2011
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Mahalanobis-based one-class classification
Patric Nader, Paul Honeine, and Pierre Beauseroy · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Lenet-5, convolutional neural networks
Yann LeCun et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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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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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Human-centric justification of machine learning predictions
Or Biran and Kathleen McKeown · 2017
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Quantile approximation of the chi–square distribution using the quantile mechanics
Hilary I Okagbue, Muminu O Adamu, and Timothy A Anake · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 2017
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Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
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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
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
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Uncertainty aware ai ml: Why and how
Lance Kaplan, Federico Cerutti, Murat Sensoy, Alun Preece, and Paul Sullivan · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Evidential deep learning to quantify classification uncertainty
Murat Sensoy, Lance Kaplan, and Melih Kandemir · 2018
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Visual interpretability for deep learning: a survey
Quanshi Zhang and Song-Chun Zhu · 2018
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