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.

本文探讨了使用单个预训练网络作为通用特征提取器，在异常检测任务中取代训练不同表示器的可能性，并在实验中证明了使用领域内数据学习表示可能不是异常检测的必要步骤。