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We consider the problem of detecting out-of-distribution images in neural networks.
On optimum recognition error and reject tradeoff
C Chow · 1970
Earlier work this paper cites.
Foundations of statistical natural language processing , volume 999
Christopher D Manning, Hinrich Schütze, et al · 1999
Earlier work this paper cites.
Manifold parzen windows
Pascal Vincent and Yoshua Bengio · 2003
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The relationship between precision-recall and roc curves
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All of Nonparametric Statistics
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Earlier work this paper cites.
Fast mining of distance-based outliers in high-dimensional datasets
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Hilbert space embeddings and metrics on probability measures
Bharath K Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, and Gert RG Lanckriet · 2010
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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.
A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
Earlier work this paper cites.
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Adam: A method for stochastic optimization
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Transfer representation-learning for anomaly detection
Jerone T.A Andrews, Thomas Tanay, Edward J. Morton, and Lewis D. Griffin · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q Weinberger · 2016
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Fully convolutional neural network for fast anomaly detection in crowded scenes
Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, et al · 2016
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets
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Very deep convolutional networks for large-scale image recognition
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A note on the evaluation of generative models
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Generative models and model criticism via optimized maximum mean discrepancy
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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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