D2AE: Data-Decoupled Active Experts Promote One-Class Anomaly Discovery

Anomaly detection (AD) suffers from severe performance decrease when dealing with corrupted datasets. By querying limited annotations from an oracle, active learning is prevalent in mitigating this problem. However, previous work ignores the particularity of the AD task on the one-class setting, where wrong pseudo-annotations of anomaly noise will mislead the active inference results. To address this challenge, we propose D 2AE, a novel active AD framework through Decoupling Data pools between training and inference process for Active Experts. Specifically, we design a data-splitting module named as DSS to obtain diverse subsets and weaken the mutual interference of similar anomalies. To decouple the data, we propose an Independent Active Experts (IAE) module formed by multiple expert replications, on which each data subset is trained by one separate expert (squad) and inferred by the other non-training ones. To further improve the efficiency of data utilization, we propose Active Expert Squad (AES) beyond IAE by introducing Mixtureof-Experts. The commonality and specificity between expert squads promote model training and active query, respectively. We conduct extensive experiments on various image, tabular, and NLP datasets. Experimental results show the superiority of our solution compared with existing methods.