Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Trained One-class Classification for Unsupervised Anomaly Detection   Anomaly detection (AD), separating anomalies from normal data, has various
applications across domains, from manufacturing to healthcare. While most
previous works have shown to be effective for cases with fully or partially
labeled data, they are less practical for AD applications due to tedious data
labeling processes. In this work, we focus on unsupervised AD problems whose
entire training data are unlabeled and may contain both normal and anomalous
samples. To tackle this problem, we build a robust one-class classification
framework via data refinement. To refine the data accurately, we propose an
ensemble of one-class classifiers, each of which is trained on a disjoint
subset of training data. Moreover, we propose a self-training of deep
representation one-class classifiers (STOC) that iteratively refines the data
and deep representations. In experiments, we show the efficacy of our method
for unsupervised anomaly detection on benchmarks from image and tabular data
domains. For example, with a 10% anomaly ratio on CIFAR-10 data, the proposed
method outperforms state-of-the-art one-class classification method by 6.3 AUC
and 12.5 average precision.

7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg