Abstract
Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their adaptability in industrial scenarios with online streaming data. Online learning-based image anomaly detection methods are more compatible with industrial online streaming data but are rarely noticed. For the first time, this paper presents a fully online learning image anomaly detection method, namely LeMO, learning memory for online image anomaly detection. LeMO leverages learnable memory initialized with orthogonal random noise, eliminating the need for excessive data in memory initialization and circumventing the inefficiencies of offline data collection. Moreover, a contrastive learning-based loss function for anomaly detection is designed to enable online joint optimization of memory and image target-oriented features. The presented method is simple and highly effective. Extensive experiments demonstrate the superior performance of LeMO in the online setting. Additionally, in the offline setting, LeMO is also competitive with the current state-of-the-art methods and achieves excellent performance in few-shot scenarios.
Abstract (translated)
现有的图像异常检测方法取得了令人印象深刻的结果，但它们大多是一种 offline 学习范式，需要在数据收集前过度收集数据，因此在工业场景下与在线 streaming 数据的连接性受到限制。基于在线学习的图像异常检测方法更适合于工业在线 streaming 数据，但它们很少被注意到。本文首次提出了一种 fully online 学习的图像异常检测方法，即 LeMO，它是一种用于在线图像异常检测的学习记忆。 LeMO 利用Orthogonal 随机噪声初始化可学习的记忆，消除了在内存初始化中需要过度数据的问题，绕过了 offline 数据收集的效率低下的问题。此外，设计了一种用于异常检测的 contrastive 学习损失函数，以便在线联合优化记忆和图像目标特征。该方法简单而高效。广泛的实验证明了 LeMO 在在线场景中的卓越性能。此外，在 offline 场景下， LeMO 也与当前的最新方法竞争，并在少量的场景中实现了出色的性能。
URL
https://arxiv.org/abs/2305.15652
