对比表征建模用于异常检测
Contrastive Representation Modeling for Anomaly Detection
AI总结：
本文提出了一种基于对比学习的表征建模方法，通过重新定义正负关系以提升异常检测性能，适用于工业场景中的局部异常检测。
摘要：
基于距离的异常检测方法依赖于紧凑的分布内（ID）嵌入，这些嵌入与异常体之间有明显分离。然而，传统对比学习策略往往难以实现这种平衡，要么导致内例之间方差过大，要么无法保持异常体的多样性。我们首先分析了异常检测中的表征学习挑战，并识别出三个必要的预设任务属性：（1）内例的紧凑聚类，（2）内例与异常体之间的强分离，（3）合成异常体之间的多样性保持。在此基础上，我们提出了一种结构化的对比目标，重新定义了训练过程中的正负关系，促进这些属性而不需显式异常标签。我们扩展了这一框架，采用基于补丁的学习和评估策略，专门设计以提高工业环境中局部异常的检测能力。我们的方法在收敛速度和性能上显著优于标准对比方法。它在语义和工业基准上匹配或超越了依赖判别训练或显式异常标签的异常检测方法。
英文摘要：
Distance-based anomaly detection methods rely on compact in-distribution (ID) embeddings that are well separated from anomalies. However, conventional contrastive learning strategies often struggle to achieve this balance, either promoting excessive variance among inliers or failing to preserve the diversity of outliers. We begin by analyzing the challenges of representation learning for anomaly detection and identify three essential properties for the pretext task: (1) compact clustering of inliers, (2) strong separation between inliers and anomalies, and (3) preservation of diversity among synthetic outliers. Building on this, we propose a structured contrastive objective that redefines positive and negative relationships during training, promoting these properties without requiring explicit anomaly labels. We extend this framework with a patch-based learning and evaluation strategy specifically designed to improve the detection of localized anomalies in industrial settings. Our approach demonstrates significantly faster convergence and improved performance compared to standard contrastive methods. It matches or surpasses anomaly detection methods on both semantic and industrial benchmarks, including methods that rely on discriminative training or explicit anomaly labels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
