Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for
  Anomaly Detection and Localization   The essence of unsupervised anomaly detection is to learn the compact
distribution of normal samples and detect outliers as anomalies in testing.
Meanwhile, the anomalies in real-world are usually subtle and fine-grained in a
high-resolution image especially for industrial applications. Towards this end,
we propose a novel framework for unsupervised anomaly detection and
localization. Our method aims at learning dense and compact distribution from
normal images with a coarse-to-fine alignment process. The coarse alignment
stage standardizes the pixel-wise position of objects in both image and feature
levels. The fine alignment stage then densely maximizes the similarity of
features among all corresponding locations in a batch. To facilitate the
learning with only normal images, we propose a new pretext task called
non-contrastive learning for the fine alignment stage. Non-contrastive learning
extracts robust and discriminating normal image representations without making
assumptions on abnormal samples, and it thus empowers our model to generalize
to various anomalous scenarios. Extensive experiments on two typical industrial
datasets of MVTec AD and BenTech AD demonstrate that our framework is effective
in detecting various real-world defects and achieves a new state-of-the-art in
industrial unsupervised anomaly detection.

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The categories of this paper are: physics
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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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: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con