通过特征相对关系提升模型对分布外样本的识别能力
CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging
- 利用样本特征与类别均值特征的误差作为判别依据
- 新方法在多个数据集上比现有最优方法高3.27% AUROC
- 适合需要安全部署深度模型的工业场景
深度神经网络在处理分布外样本时常表现出过度自信，亟需有效的分布外检测以保障实际应用中的安全性。现有后处理方法主要通过重塑样本特征增强对数概率方法的判别力，但忽略了特征本身蕴含的关键信息。本文提出基于类别感知相对特征的方法（CARef），以样本特征与其类别均值特征之间的误差作为判别标准。为进一步优化，引入类别感知解耦相对特征方法（CADRef），根据相对特征与模型权重符号的一致性对特征进行解耦，增强判别能力。在多个数据集和模型上的大量实验表明，所提方法在分布外检测中表现更优且更具鲁棒性。具体而言，两项方法在AUROC上分别优于最佳基线2.82%和3.27%，在FPR95上分别提升4.03%和6.32%。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) have been widely criticized for their overconfidence when dealing with out-of-distribution (OOD) samples, highlighting the critical need for effective OOD detection to ensure the safe deployment of DNNs in real-world settings. Existing post-hoc OOD detection methods primarily enhance the discriminative power of logit-based approaches by reshaping sample features, yet they often neglect critical information inherent in the features themselves. In this paper, we propose the Class-Aware Relative Feature-based method (CARef), which utilizes the error between a sample's feature and its class-aware average feature as a discriminative criterion. To further refine this approach, we introduce the Class-Aware Decoupled Relative Feature-based method (CADRef), which decouples sample features based on the alignment of signs between the relative feature and corresponding model weights, enhancing the discriminative capabilities of CARef. Extensive experimental results across multiple datasets and models demonstrate that both proposed methods exhibit effectiveness and robustness in OOD detection compared to state-of-the-art methods. Specifically, our two methods outperform the best baseline by 2.82% and 3.27% in AUROC, with improvements of 4.03% and 6.32% in FPR95, respectively.
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