Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Do We Really Need to Learn Representations from In-domain Data for
  Outlier Detection?   Unsupervised outlier detection, which predicts if a test sample is an outlier
or not using only the information from unlabelled inlier data, is an important
but challenging task. Recently, methods based on the two-stage framework
achieve state-of-the-art performance on this task. The framework leverages
self-supervised representation learning algorithms to train a feature extractor
on inlier data, and applies a simple outlier detector in the feature space. In
this paper, we explore the possibility of avoiding the high cost of training a
distinct representation for each outlier detection task, and instead using a
single pre-trained network as the universal feature extractor regardless of the
source of in-domain data. In particular, we replace the task-specific feature
extractor by one network pre-trained on ImageNet with a self-supervised loss.
In experiments, we demonstrate competitive or better performance on a variety
of outlier detection benchmarks compared with previous two-stage methods,
suggesting that learning representations from in-domain data may be unnecessary
for outlier detection.

22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio