Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining   Detecting out-of-distribution (OOD) inputs is critical for safely deploying
deep learning models in an open-world setting. However, existing OOD detection
solutions can be brittle in the open world, facing various types of adversarial
OOD inputs. While methods leveraging auxiliary OOD data have emerged, our
analysis on illuminative examples reveals a key insight that the majority of
auxiliary OOD examples may not meaningfully improve or even hurt the decision
boundary of the OOD detector, which is also observed in empirical results on
real data. In this paper, we provide a theoretically motivated method,
Adversarial Training with informative Outlier Mining (ATOM), which improves the
robustness of OOD detection. We show that, by mining informative auxiliary OOD
data, one can significantly improve OOD detection performance, and somewhat
surprisingly, generalize to unseen adversarial attacks. ATOM achieves
state-of-the-art performance under a broad family of classic and adversarial
OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset,
ATOM reduces the FPR (at TPR 95%) by up to 57.99% under adversarial OOD inputs,
surpassing the previous best baseline by a large margin.

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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex