Dynamic Expert Routing for Unsupervised Continual Anomaly Detection

Yuxuan Cai, Xinwei He, Ao Tong, Xiang Bai

Abstract: Unsupervised continual anomaly detection (UCAD) aims to develop a model that can continuously learn in dynamic scenarios while avoiding forgetting previously acquired knowledge from past tasks, enabling effective anomaly detection in both past and present tasks. Recently, some works have attempted to alleviate forgetting by exploiting knowledge banks to preserve past knowledge. However, they are proven to be time-consuming, limiting their practical applicability. In this article, we propose a new framework called Dynamic Expert Routing (DER) for UCAD. The key idea is to construct a task-specific expert for each anomaly detection task during training, and dynamically select the appropriate expert during inference. To build experts for anomaly detection, DER first employs a shared pre-trained encoder for feature extraction across all tasks. On top of that, an adaptor and a learnable prompt are then jointly introduced for each task. Meanwhile, in UCAD, the task identity of the sample is unknown during inference, making it challenging to select the appropriate expert. To address this issue, we propose an Adaptive Selection Module that dynamically determines the task identity based on the sample’s semantic representation. DER achieves encouraging UCAD performance in terms of accuracy and inference speed. For instance, it advances the state-of-the-art under both the MVTec-MCCL and MVTec-KSDD settings. Meanwhile, it performs 3× faster than the previous art with comparable parameters.

External IDs:dblp:journals/tii/CaiHTB25