Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection
This work addresses the need for better benchmarks in continual anomaly detection for real-world deployment, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of continual learning in anomaly detection by introducing a large-scale benchmark called Continual-MEGA, which includes a new dataset and a zero-shot generalization scenario, and reported that their proposed method consistently outperforms prior approaches with improvements in pixel-level defect localization.
In this paper, we introduce a new benchmark for continual learning in anomaly detection, aimed at better reflecting real-world deployment scenarios. Our benchmark, Continual-MEGA, includes a large and diverse dataset that significantly expands existing evaluation settings by combining carefully curated existing datasets with our newly proposed dataset, ContinualAD. In addition to standard continual learning with expanded quantity, we propose a novel scenario that measures zero-shot generalization to unseen classes, those not observed during continual adaptation. This setting poses a new problem setting that continual adaptation also enhances zero-shot performance. We also present a unified baseline algorithm that improves robustness in few-shot detection and maintains strong generalization. Through extensive evaluations, we report three key findings: (1) existing methods show substantial room for improvement, particularly in pixel-level defect localization; (2) our proposed method consistently outperforms prior approaches; and (3) the newly introduced ContinualAD dataset enhances the performance of strong anomaly detection models. We release the benchmark and code in https://github.com/Continual-Mega/Continual-Mega.