Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sub-Image Anomaly Detection with Deep Pyramid Correspondences   Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit
very strong anomaly detection performance when applied to entire images. A
limitation of kNN methods is the lack of segmentation map describing where the
anomaly lies inside the image. In this work we present a novel anomaly
segmentation approach based on alignment between an anomalous image and a
constant number of the similar normal images. Our method, Semantic Pyramid
Anomaly Detection (SPADE) uses correspondences based on a multi-resolution
feature pyramid. SPADE is shown to achieve state-of-the-art performance on
unsupervised anomaly detection and localization while requiring virtually no
training time.

12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph