  Active learning has been utilized as an efficient tool in building anomaly
detection models by leveraging expert feedback. In an active learning
framework, a model queries samples to be labeled by experts and re-trains the
model with the labeled data samples. It unburdens in obtaining annotated
datasets while improving anomaly detection performance. However, most of the
existing studies focus on helping experts identify as many abnormal data
samples as possible, which is a sub-optimal approach for one-class
classification-based deep anomaly detection. In this paper, we tackle two
essential problems of active learning for Deep SVDD: query strategy and
semi-supervised learning method. First, rather than solely identifying
anomalies, our query strategy selects uncertain samples according to an
adaptive boundary. Second, we apply noise contrastive estimation in training a
one-class classification model to incorporate both labeled normal and abnormal
data effectively. We analyze that the proposed query strategy and
semi-supervised loss individually improve an active learning process of anomaly
detection and further improve when combined together on seven anomaly detection
datasets.
