Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-grained Anomaly Detection via Multi-task Self-Supervision   Detecting anomalies using deep learning has become a major challenge over the
last years, and is becoming increasingly promising in several fields. The
introduction of self-supervised learning has greatly helped many methods
including anomaly detection where simple geometric transformation recognition
tasks are used. However these methods do not perform well on fine-grained
problems since they lack finer features. By combining in a multi-task framework
high-scale shape features oriented task with low-scale fine features oriented
task, our method greatly improves fine-grained anomaly detection. It
outperforms state-of-the-art with up to 31% relative error reduction measured
with AUROC on various anomaly detection problems.

11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom