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Despite the recent developments that allowed neural networks to achieve impressive performance on a variety of applications, these models are intrinsically affected by the problem of overgeneralization, due to their partitioning of the full input space into the fixed set of target classes used during training.
The mnist database of handwritten digits
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Support vector method for novelty detection
Bernhard Schölkopf, Robert C Williamson, Alex J Smola, John Shawe-Taylor, and John C Platt · 2000
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Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson · 2001
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Novelty detection: a review - part 1: statistical approaches
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A computable plug-in estimator of minimum volume sets for novelty detection
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Walter J Scheirer, Lalit P Jain, and Terrance E Boult · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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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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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2016
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Mitigating fooling with competitive overcomplete output layer neural networks
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