用最优传输学习局部与全局原型，提升无监督异常检测精度。
Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization
- 通过最优传输融合特征与空间代价，学习图像的局部和全局原型。
- 在两个工业图像基准上性能媲美强基线模型。
- 适合需要无监督检测细微缺陷的工业质检场景。
无监督异常检测旨在仅使用正常样本训练数据的情况下，识别样本中的缺陷区域。该技术在工业检测和医学影像等领域具有广泛应用，因为标注成本高或避免引入特定异常类型的偏差。本文提出一种基于原型学习的新方法，引入一种结合特征代价与空间代价的度量，利用预训练图像编码器提取的潜在表示，通过最优传输学习局部和全局原型。实验表明，该方法能施加结构约束，捕捉正常样本的内在组织规律，从而更有效发现图像中的不一致性。模型在两个工业图像异常检测基准上表现优异，达到与强基线相当的性能。
原文摘要 · Abstract (English)
Unsupervised anomaly detection aims to detect defective parts of a sample by having access, during training, to a set of normal, i.e. defect-free, data. It has many applications in fields, such as industrial inspection or medical imaging, where acquiring labels is costly or when we want to avoid introducing biases in the type of anomalies that can be spotted. In this work, we propose a novel UAD method based on prototype learning and introduce a metric to compare a structured set of embeddings that balances a feature-based cost and a spatial-based cost. We leverage this metric to learn local and global prototypes with optimal transport from latent representations extracted with a pre-trained image encoder. We demonstrate that our approach can enforce a structural constraint when learning the prototypes, allowing to capture the underlying organization of the normal samples, thus improving the detection of incoherencies in images. Our model achieves performance that is on par with strong baselines on two reference benchmarks for anomaly detection on industrial images.
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