用伪标签构建子空间表示，提升分布外样本检测的鲁棒性
Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detection
- 通过伪标签引导学习数据在子空间中的表示
- 结合分类损失与子空间距离正则，增强分布内/外样本分离
- 适合需要高可靠性检测的工业级AI系统
分布外（OOD）检测是实现鲁棒人工智能的核心任务，旨在识别训练集之外的新分布样本。现有方法多依赖特征空间中的严格假设，限制了分布内（ID）与分布外（OOD）样本的可分性。本文提出一种基于伪标签诱导子空间表示的新型OOD检测框架，在更宽松自然的假设下工作。同时引入一种简单有效的学习准则，融合基于交叉熵的ID分类损失与基于子空间距离的正则化损失，以提升ID与OOD样本的可分性。大量实验证明该框架的有效性。
原文摘要 · Abstract (English)
Out-of-distribution (OOD) detection lies at the heart of robust artificial intelligence (AI), aiming to identify samples from novel distributions beyond the training set. Recent approaches have exploited feature representations as distinguishing signatures for OOD detection. However, most existing methods rely on restrictive assumptions on the feature space that limit the separability between in-distribution (ID) and OOD samples. In this work, we propose a novel OOD detection framework based on a pseudo-label-induced subspace representation, that works under more relaxed and natural assumptions compared to existing feature-based techniques. In addition, we introduce a simple yet effective learning criterion that integrates a cross-entropy-based ID classification loss with a subspace distance-based regularization loss to enhance ID-OOD separability. Extensive experiments validate the effectiveness of our framework.
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