Detecting abnormal patterns that deviate from a certain regular repeating
pattern in time series is essential in many big data applications. However, the
lack of labels, the dynamic nature of time series data, and unforeseeable
abnormal behaviors make the detection process challenging. Despite the success
of recent deep anomaly detection approaches, the mystical mechanisms in such
black-box models have become a new challenge in safety-critical applications.
The lack of model transparency and prediction reliability hinders further
breakthroughs in such domains. This paper proposes ProtoAD, using prototypes as
the example-based explanation for the state of regular patterns during anomaly
detection. Without significant impact on the detection performance, prototypes
shed light on the deep black-box models and provide intuitive understanding for
domain experts and stakeholders. We extend the widely used prototype learning
in classification problems into anomaly detection. By visualizing both the
latent space and input space prototypes, we intuitively demonstrate how regular
data are modeled and why specific patterns are considered abnormal.

该论文提出了一种名为 ProtoAD 的模型，使用基于示例的原型来解释深度模型中的正常模式状态，旨在提高深度模型的透明度和可理解性，进一步提高异常检测的精度和性能。