理解对比表征学习中的归一化及其在分布外检测中的应用
Understanding normalization in contrastive representation learning and out-of-distribution detection
AI总结：
本文研究对比特征的 $\ell_2$ 范数，提出通过在对比层空间区分正常样本纳入分布外数据的方法，可用于 OE 或完全自监督设置，并在多类图像与单/多模态实验中验证其优越性。
摘要：
对比表征学习已成为一种出色的异常检测方法。在本工作中，我们探究对比特征的 $\ell_2$ 范数及其在分布外检测中的应用。我们提出一种基于对比学习的简单方法，该方法通过在对比层空间中区分正常样本来纳入分布外数据。我们的方法可以灵活地作为异常值暴露（OE）方法应用，此时分布外数据是大量随机图像的集合；也可以作为完全自监督学习方法应用，此时分布外数据通过施加分布偏移变换自行生成。纳入额外分布外样本的能力，为基于对比学习的异常检测（AD）方法通常表现不佳的数据集（如航空图像或显微图像）提供了可行的解决方案。此外，通过对比学习学到的高质量特征能够持续提升 OE 场景下的性能，即使可用的分布外数据集多样性不足也是如此。我们的大量实验表明，所提出的方法在多种场景下（包括单模态和多模态设置，以及多种图像数据集）具有优越性。
英文摘要：
Contrastive representation learning has emerged as an outstanding approach for anomaly detection. In this work, we explore the $\ell_2$-norm of contrastive features and its applications in out-of-distribution detection. We propose a simple method based on contrastive learning, which incorporates out-of-distribution data by discriminating against normal samples in the contrastive layer space. Our approach can be applied flexibly as an outlier exposure (OE) approach, where the out-of-distribution data is a huge collective of random images, or as a fully self-supervised learning approach, where the out-of-distribution data is self-generated by applying distribution-shifting transformations. The ability to incorporate additional out-of-distribution samples enables a feasible solution for datasets where AD methods based on contrastive learning generally underperform, such as aerial images or microscopy images. Furthermore, the high-quality features learned through contrastive learning consistently enhance performance in OE scenarios, even when the available out-of-distribution dataset is not diverse enough. Our extensive experiments demonstrate the superiority of our proposed method under various scenarios, including unimodal and multimodal settings, with various image datasets.
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。
