Unsupervised deep learning techniques are widely used to identify anomalous
behaviour. The performance of such methods is a product of the amount of
training data and the model size. However, the size is often a limiting factor
for the deployment on resource-constrained devices. We present a novel
procedure based on knowledge distillation for compressing an unsupervised
anomaly detection model into a supervised deployable one and we suggest a set
of techniques to improve the detection sensitivity. Compressed models perform
comparably to their larger counterparts while significantly reducing the size
and memory footprint.

无监督深度学习技术广泛用于识别异常行为。该研究介绍了一种基于知识蒸馏的新型过程，用于将无监督异常检测模型压缩成监督可部署模型，并提出了一套提高检测敏感性的技术集合。压缩模型在显著减小大小和内存占用的同时，性能与较大模型相当。

知识蒸馏用于异常检测

Knowledge Distillation for Anomaly Detection

The detection of anomalous behaviours is an emerging need in many
applications, particularly in contexts where security and reliability are
critical aspects. While the definition of anomaly strictly depends on the
domain framework, it is often impractical or too time consuming to obtain a
fully labelled dataset. The use of unsupervised models to overcome the lack of
labels often fails to catch domain specific anomalies as they rely on general
definitions of outlier. This paper suggests a new active learning based
approach, ALIF, to solve this problem by reducing the number of required labels
and tuning the detector towards the definition of anomaly provided by the user.
The proposed approach is particularly appealing in the presence of a Decision
Support System (DSS), a case that is increasingly popular in real-world
scenarios. While it is common that DSS embedded with anomaly detection
capabilities rely on unsupervised models, they don't have a way to improve
their performance: ALIF is able to enhance the capabilities of DSS by
exploiting the user feedback during common operations. ALIF is a lightweight
modification of the popular Isolation Forest that proved superior performances
with respect to other state-of-art algorithms in a multitude of real anomaly
detection datasets.

本文提出了一种基于主动学习的方法（ALIF），用于检测具有领域特定异常定义的不寻常行为，并且性能优于其他最先进的算法。该方法特别适用于集成有异常检测功能的决策支持系统（DSS）。