用度量学习和熵评分实现少样本可解释异常检测
MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring
- 基于度量学习与可训练熵评分，无需假设异常分布
- 仅需少量异常样本即可训练，且不依赖数据增强
- 提供可视化解释，适合需要可信检测的工业场景
异常检测在多媒体应用中至关重要，用于发现缺陷产品并实现自动化质量检查。深度学习模型通常需要大量标注数据，但异常样本稀少导致数据极度不平衡。且模型的黑箱特性难以获得用户信任。为此，我们提出MeLIAD，一种基于度量学习的可解释异常检测新方法，无需预先假设真实异常的分布。该方法仅需少量异常样本进行训练，不使用任何数据增强技术，且从设计上具备可解释性，可提供图像异常原因的可视化解释。其核心在于引入可训练的熵基评分组件以识别和定位异常实例，并设计联合优化评分组件与度量学习目标的新损失函数。在五个公开基准数据集上的实验表明，包括定量与定性可解释性评估，MeLIAD在异常检测与定位性能上均优于现有先进方法。
原文摘要 · Abstract (English)
Anomaly detection (AD) plays a pivotal role in multimedia applications for detecting defective products and automating quality inspection. Deep learning (DL) models typically require large-scale annotated data, which are often highly imbalanced since anomalies are usually scarce. The black box nature of these models prohibits them from being trusted by users. To address these challenges, we propose MeLIAD, a novel methodology for interpretable anomaly detection, which unlike the previous methods is based on metric learning and achieves interpretability by design without relying on any prior distribution assumptions of true anomalies. MeLIAD requires only a few samples of anomalies for training, without employing any augmentation techniques, and is inherently interpretable, providing visualizations that offer insights into why an image is identified as anomalous. This is achieved by introducing a novel trainable entropy-based scoring component for the identification and localization of anomalous instances, and a novel loss function that jointly optimizes the anomaly scoring component with a metric learning objective. Experiments on five public benchmark datasets, including quantitative and qualitative evaluation of interpretability, demonstrate that MeLIAD achieves improved anomaly detection and localization performance compared to state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。