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.">

Dynamic Expert Routing for Unsupervised Continual Anomaly Detection (original) (raw)

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