Convolutional Neural Network (CNN) techniques have proven to be very useful
in image-based anomaly detection applications. CNN can be used as deep features
extractor where other anomaly detection techniques are applied on these
features. For this scenario, using transfer learning is common since pretrained
models provide deep feature representations that are useful for anomaly
detection tasks. Consequentially, anomaly can be detected by applying similarly
measure between extracted features and a defined model of normality. A key
factor in such approaches is the decision threshold used for detecting anomaly.
While most of the proposed methods focus on the approach itself, slight
attention has been paid to address decision threshold settings. In this paper,
we tackle this problem and propose a welldefined method to set the
working-point decision threshold that improves detection accuracy. We introduce
a transfer learning framework for anomaly detection based on similarity measure
with a Model of Normality (MoN) and show that with the proposed threshold
settings, a significant performance improvement can be achieved. Moreover, the
framework has low complexity with relaxed computational requirements.

本文提出了一种基于相似性度量将深度卷积神经网络作为特征提取器用于异常检测的迁移学习框架，并通过对工作点决策阈值的优化来提高检测准确率。