Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Out-of-distribution Detection in Classifiers via Generation   By design, discriminatively trained neural network classifiers produce
reliable predictions only for in-distribution samples. For their real-world
deployments, detecting out-of-distribution (OOD) samples is essential. Assuming
OOD to be outside the closed boundary of in-distribution, typical neural
classifiers do not contain the knowledge of this boundary for OOD detection
during inference. There have been recent approaches to instill this knowledge
in classifiers by explicitly training the classifier with OOD samples close to
the in-distribution boundary. However, these generated samples fail to cover
the entire in-distribution boundary effectively, thereby resulting in a
sub-optimal OOD detector. In this paper, we analyze the feasibility of such
approaches by investigating the complexity of producing such "effective" OOD
samples. We also propose a novel algorithm to generate such samples using a
manifold learning network (e.g., variational autoencoder) and then train an n+1
classifier for OOD detection, where the $n+1^{th}$ class represents the OOD
samples. We compare our approach against several recent classifier-based OOD
detectors on MNIST and Fashion-MNIST datasets. Overall the proposed approach
consistently performs better than the others.

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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th