Adaptive aggregation-distillation autoencoder for unsupervised anomaly detection. (November 2022) - Zhu, Jiaqi; Deng, Fang; Zhao, Jiachen; Chen, Jie
Abstract : We propose the adaptive aggregation-distillation autoencoder for unsupervised anomaly detection, which considers the diversity of normal patterns and provides a strong guarantee for anomaly detection during training sets containing anomalies. A density-based landmark is designed to represent diverse normal patterns, which can adaptively update the location and quantity of landmarks during training. An aggregation-distillation mechanism is built upon the landmark selection in respect to landmark-guided convex polygon reconstruction for minimizing the intra-class variation and differentiating normal from abnormal patterns. We achieve the state-of-the-art performance on standard benchmarks for unsupervised anomaly detection in ten real-world datasets from different application domains. Abstract: Anomaly detection (AD) has been receiving great attention as it plays a crucial role in many areas of basic research and industrial applications. However, most existing AD methods not only rely on training on normal data, but also ignore the multi-cluster nature of normal and abnormal patterns. To overcome these limitations, this paper proposes a novel method called Adaptive Aggregation-Distillation AutoEncoder (AADAE) for unsupervised anomaly detection. AADAE is built upon the density-based landmark selection in respect to representing diverse normal patterns. During training, AADAE adaptively updates the location and quantity of landmarks. Then, an aggregation-distillation mechanism is constructed: Firstly, it aggregates the latent representations of normal and anomalous to different landmark-guided regions within the convex polygon with landmarks as vertices, which minimizes the intra-class variation and promotes the separability of normal and abnormal samples. Secondly, the distillation mechanism is applied to obtain reliable detection results when there are anomalies in the training set. The aggregation process motivates AADAE to learn the distribution of multi-cluster normal samples with the help of landmarks, which in turn facilitates the distillation process to differentiate normal from anomalies for training. Extensive empirical studies on ten datasets from different application domains demonstrate the efficiency and generalization ability of the method.