用可解释的边界提升异常检测准确率
Interpretable Maximum Margin Deep Anomaly Detection
- 引入小规模标注异常数据与最大间隔优化，稳定训练过程
- 在图像和表格数据集上优于多个先进基线模型
- 能端到端学习中心与半径，结果可可视化，适合需要透明决策的场景
异常检测是广泛应用于机器学习的重要任务。深度支持向量数据描述（Deep SVDD）是一种主流的深度单类方法，但易受超球体坍塌影响，常依赖启发式选择超球体参数，且可解释性有限。为此，我们提出可解释的最大间隔深度异常检测（IMD-AD），利用少量标注异常样本和最大间隔目标，稳定训练并增强判别能力，天然抵御超球体坍塌。我们证明了超球体参数与网络最后一层权重之间的等价性，使中心和半径可端到端学习，实现内在可解释性与可视化输出。我们进一步设计了一种高效训练算法，联合优化表示、间隔与最后一层参数。在图像与表格基准上的大量实验与消融研究显示，IMD-AD在性能上超越多个先进基线，同时提供可解释的决策诊断。
原文摘要 · Abstract (English)
Anomaly detection is a crucial machine-learning task with wide-ranging applications. Deep Support Vector Data Description (Deep SVDD) is a prominent deep one-class method, but it is vulnerable to hypersphere collapse, often relies on heuristic choices for hypersphere parameters, and provides limited interpretability. To address these issues, we propose Interpretable Maximum Margin Deep Anomaly Detection (IMD-AD), which leverages a small set of labeled anomalies and a maximum margin objective to stabilize training and improve discrimination. It is inherently resilient to hypersphere collapse. Furthermore, we prove an equivalence between hypersphere parameters and the network's final-layer weights, which allows the center and radius to be learned end-to-end as part of the model and yields intrinsic interpretability and visualizable outputs. We further develop an efficient training algorithm that jointly optimizes representation, margin, and final-layer parameters. Extensive experiments and ablation studies on image and tabular benchmarks demonstrate that IMD-AD empirically improves detection performance over several state-of-the-art baselines while providing interpretable decision diagnostics.
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