用于视觉异常检测的深度卷积大间隔 $\ell_p$-SVDD

中文摘要 AI 辅助

视觉异常检测需要自适应表示和可靠的决策边界，尤其是在异常训练样本稀缺且类分布高度不平衡时。经典基于核的方法产生有原则的几何决策区域，但通常在固定特征上操作，而深度检测器学习特定任务表示但常无法提供明确的边缘感知核边界。本研究提出DLM-SVDD，一个联合学习卷积特征和基于核的明确决策边界的深度大间隔新颖性检测框架。通过利用大间隔 $\ell_p$-支持向量数据描述方法，该方法在使表示适应目标任务时进行明确的边缘最大化和非线性松弛惩罚。为训练模型，提出一种优化方案，在基于Frank-Wolfe的凸对偶边界更新和基于恢复边界引起的平滑边缘违反损失的CNN更新步骤之间交替。为提高可扩展性，分析不同核近似策略的效率-准确性权衡，得出大规模异常检测的实用建议。在多个标准基准上的广泛实验表明，与基线相比性能持续提升，与现有方法相比整体性能强劲，同时表明所提出的联合表示-边界学习方案在严重不平衡类分布下仍有效。

英文摘要

Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are highly imbalanced. Classical kernel-based methods yield principled geometric decision regions but typically operate on fixed features, while deep detectors learn task-specific representations but often fail to provide an explicit margin-aware kernel boundary. In this study, we propose DLM-SVDD, a deep large-margin novelty-detection framework that jointly learns convolutional features and an explicit kernel-based decision boundary. By drawing on the large-margin $\ell_p$-Support Vector Data Description ($\ell_p$-SVDD) approach, the proposed method performs explicit margin maximization and nonlinear slack penalization while adapting the representation to the target task. To train the proposed model, we present an optimization scheme that alternates between a Frank--Wolfe--based update of the convex dual boundary and a CNN update step operating on a smooth margin-violation loss induced by the recovered boundary. To improve scalability, we analyze the efficiency--accuracy trade-offs for different kernel approximation strategies, deriving practical propositions for large-scale anomaly detection. Extensive experiments on multiple standard benchmarks show consistent performance improvements over the baseline and strong overall performance compared with state-of-the-art methods while illustrating that the proposed joint representation--boundary learning scheme remains effective under severe imbalanced class distributions.

发表机构

• Bilkent University（比尔肯特大学）

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