可解释多超球深度异常检测用于开放集监督异常检测

中文摘要 AI 辅助

多类开放集异常检测要求模型仅利用来自已知正常类别的类标记样本，刻画由多个异构子分布形成的正常接受域，并在测试时识别出以前未见过的异常。现有的单超球方法无法显式表示类特定的位置和接受范围，而当前的多超球或多类方法并未完全整合类间边界约束、可学习的接受范围和可解释的决策。为解决这些局限性，我们提出了可解释多超球深度异常检测（IMHD-AD）。IMHD-AD在共享特征空间中为每个已知正常类别构建一个独立的超球。通过目标在内和非目标在外的约束，IMHD-AD将类特定的超球中心和半径直接嵌入最终网络层，并与共享表示联合优化。跨超球的最小带符号边界分数同时决定开放集接受或拒绝，并为每个决策提供忠实的几何解释。在MNIST、Fashion-MNIST和CIFAR-10上，IMHD-AD在30个开放集比较中的28个中取得了最高的AUC。一项二维合成研究进一步表明，模型架构必须在已知正常类别的紧凑性与未知异常的可分离性之间取得平衡。

英文摘要

Multi-class open-set anomaly detection requires a model to characterize the normal acceptance domain formed by multiple heterogeneous subdistributions using only class-labeled samples from known normal classes, and to identify previously unseen anomalies at test time. Existing single-hypersphere methods cannot explicitly represent class-specific locations and acceptance ranges, while current multi-hypersphere or multi-class approaches do not fully integrate inter-class boundary constraints, learnable acceptance ranges, and interpretable decisions. To address these limitations, we propose Interpretable Multi-Hypersphere Deep Anomaly Detection (IMHD-AD). IMHD-AD constructs an independent hypersphere for each known normal class in a shared feature space. With target-inside and non-target-outside constraints, IMHD-AD embeds the class-specific hypersphere centers and radii directly into the final network layer and jointly optimizes them with the shared representation. The minimum signed boundary score across hyperspheres simultaneously determines open-set acceptance or rejection and provides a faithful geometric explanation of each decision. On MNIST, Fashion-MNIST, and CIFAR-10, IMHD-AD achieves the highest AUC in 28 of 30 open-set comparisons. A two-dimensional synthetic study further shows that model architecture must balance the compactness of known normal classes against the separability of unknown anomalies.

发表机构

• Yunnan University of Finance and Economics（云南财经大学）
• Capital Normal University（首都师范大学）

机构由 AI 辅助整理，请以论文原文为准。