双原型引导的多任务学习用于无监督异常检测与分类
Dual-Prototype-Guided Multi-task Learning for Unsupervised Anomaly Detection and Classification
对做工业/医学异常检测的人，这篇提供了用正常原型与类别原型做显式特征解耦、并用差分门控交互与几何正则缓解多任务梯度冲突的框架，可直接迁移到需要同时定位异常区域并判别异常类型的视觉检测系统。
原文依据 we propose the Prototype-Guided Semi-Supervised Feature Disentanglement (PG-SFD) framework
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无监督异常检测（UAD）与异常分类常用于工业与医学场景。具体而言，UAD 在细粒度像素级识别异常区域，而异常分类在异常区域层面区分异常类型。然而，现有方法通常将这两个任务独立且顺序地处理，忽视了联合训练二者以抑制局部视觉歧义（LVA）的益处，该歧义源于不同类型异常在局部视觉模式上的相似性。此外，多任务学习框架无法直接用于联合训练这两个任务，因为 UAD 与异常分类存在特征偏好不兼容的问题。为解决上述局限，我们提出原型引导的半监督特征解耦（PG-SFD）框架，实现从隐式特征共享到显式特征解耦的范式转变，并通过双原型解耦模块（DPRM）显式构造正常原型与类别原型，以消除隐式的正常-异常语义耦合。此外，为实现跨任务特征差分注入与梯度冲突缓解，我们提出差分门控交互（DGI）与几何正则化优化（GRO），与 DPRM 共同构成一个内聚的框架。PG-SFD 在 UAD 任务与弱监督分类任务上均表现出很高的有效性。同时，它在包括工业与医学数据集在内的多类数据集上性能稳定，表明其具有很强的泛化能力。
英文原文摘要 1604 字符
Unsupervised Anomaly Detection (UAD) and anomaly classification are frequently used in industrial and medical scenarios. Specifically, UAD identifies anomalous regions at the fine-grained pixel-level, while anomaly classification distinguishes anomaly types at the anomaly region level. However, existing approaches typically treat these tasks independently and sequentially, overlooking the benefits of jointly training them to suppress Local Visual Ambiguity (LVA) caused by the similarities of different types of anomalies in local visual patterns. Moreover, a multi-task learning framework cannot be directly applied to jointly train the two tasks, since UAD and anomaly classification exhibit feature preference incompatibility. To address these limitations, we propose the Prototype-Guided Semi-Supervised Feature Disentanglement (PG-SFD) framework, which makes a paradigm shift from implicit feature sharing to explicit feature disentanglement and explicitly constructs normal and category prototypes to eliminate implicit normal-abnormal semantic coupling via a Dual-Prototype Disentanglement Module (DPRM). Moreover, for cross-task feature differential injection and gradient conflict mitigation, the Differential Gated Interaction (DGI) and Geometry-Regularized Optimization (GRO) are proposed to form a cohesive framework with DPRM. PG-SFD demonstrates high effectiveness in both UAD tasks and weakly supervised classification tasks. Meanwhile, it exhibits stable performance across multiple types of datasets, including industrial and medical datasets, indicating its strong generalizability.