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Neural networks are known to produce over-confident predictions on input images, even when these images are out-of-distribution (OOD) samples.
A tutorial on energy-based learning
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Reading digits in natural images with unsupervised feature learning
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao · 2015
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On the importance of gradients for detecting distributional shifts in the wild
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Energy-based out-of-distribution detection
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Atom: Robustifying out-of-distribution detection using outlier mining
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Outlier exposure with confidence control for out-of-distribution detection
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