Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Anomaly Detection with Outlier Exposure   It is important to detect anomalous inputs when deploying machine learning
systems. The use of larger and more complex inputs in deep learning magnifies
the difficulty of distinguishing between anomalous and in-distribution
examples. At the same time, diverse image and text data are available in
enormous quantities. We propose leveraging these data to improve deep anomaly
detection by training anomaly detectors against an auxiliary dataset of
outliers, an approach we call Outlier Exposure (OE). This enables anomaly
detectors to generalize and detect unseen anomalies. In extensive experiments
on natural language processing and small- and large-scale vision tasks, we find
that Outlier Exposure significantly improves detection performance. We also
observe that cutting-edge generative models trained on CIFAR-10 may assign
higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to
mitigate this issue. We also analyze the flexibility and robustness of Outlier
Exposure, and identify characteristics of the auxiliary dataset that improve
performance.

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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th