BootOOD: 通过神经崩溃下合成样本暴露实现自监督的分布外检测
BootOOD: Self-Supervised Out-of-Distribution Detection via Synthetic Sample Exposure under Neural Collapse
AI总结：
BootOOD通过自监督学习和神经崩溃特性，实现高效分布外检测，在保持ID准确性的同时超越现有方法。
摘要：
分布外（OOD）检测对于在安全敏感环境中部署图像分类器至关重要，但现有检测器在OOD样本在语义上与分布内（ID）类别相似时往往表现不佳。我们提出了BootOOD，一个完全自监督的OOD检测框架，该框架仅从ID数据中进行 bootstrap，并专门设计以处理语义上具有挑战性的OOD样本。BootOOD通过简单的ID表示转换合成伪OOD特征，并利用神经崩溃（NC），其中ID特征紧密聚集在类别均值周围，具有一致的特征范数。与以往旨在将OOD特征限制在与坍缩ID均值正交的子空间中的方法不同，BootOOD引入了一个轻量级的辅助头，通过基于半径的分类对特征范数进行分类。这种设计将OOD检测与主分类器解耦，并施加了更宽松的要求：OOD样本被学习为具有比ID特征更小的特征范数，这在ID和OOD语义接近时更容易满足。在CIFAR-10、CIFAR-100和ImageNet-200上的实验表明，BootOOD在性能上优于以往的后验方法，超越了没有异常值暴露的训练方法，并在与最新异常值暴露方法竞争时保持或提高了ID准确性。
英文摘要：
Out-of-distribution (OOD) detection is critical for deploying image classifiers in safety-sensitive environments, yet existing detectors often struggle when OOD samples are semantically similar to the in-distribution (ID) classes. We present BootOOD, a fully self-supervised OOD detection framework that bootstraps exclusively from ID data and is explicitly designed to handle semantically challenging OOD samples. BootOOD synthesizes pseudo-OOD features through simple transformations of ID representations and leverages Neural Collapse (NC), where ID features cluster tightly around class means with consistent feature norms. Unlike prior approaches that aim to constrain OOD features into subspaces orthogonal to the collapsed ID means, BootOOD introduces a lightweight auxiliary head that performs radius-based classification on feature norms. This design decouples OOD detection from the primary classifier and imposes a relaxed requirement: OOD samples are learned to have smaller feature norms than ID features, which is easier to satisfy when ID and OOD are semantically close. Experiments on CIFAR-10, CIFAR-100, and ImageNet-200 show that BootOOD outperforms prior post-hoc methods, surpasses training-based methods without outlier exposure, and is competitive with state-of-the-art outlier-exposure approaches while maintaining or improving ID accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
