Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection

TL;DR

This paper introduces Continual-MEGA, a comprehensive benchmark for continual anomaly detection that includes a large dataset and a new zero-shot generalization scenario, aiming to improve robustness and real-world applicability.

Contribution

The paper presents a new large-scale benchmark, Continual-MEGA, with a novel zero-shot generalization scenario and a unified baseline algorithm for continual anomaly detection.

Findings

Existing methods need improvement, especially in pixel-level defect localization.

The proposed method outperforms prior approaches.

The ContinualAD dataset improves anomaly detection performance.

Abstract

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.