Ziang Cui

Towards Lightweight, Model-Agnostic and Diversity-Aware Active Anomaly Detection

Active Anomaly Discovery (AAD) is flourishing in the anomaly detection research area, which aims to incorporate analysts’ feedback into unsupervised anomaly detectors. However, existing AAD approaches usually prioritize the samples with the highest anomaly scores for user labeling, which hinders the…