通过多阈值化重新思考无监督离群点检测
Rethinking Unsupervised Outlier Detection via Multiple Thresholding
AI总结：
针对无监督图像离群点检测中阈值确定难、无预测标签限制应用与自监督增强的问题，提出多阈值化模块分离内点与离群点以优化特征，有效提升离群点打分方法性能，还让朴素距离法达到SOTA。
摘要：
在无监督图像离群点检测领域，分配离群点分数比其后续任务——用于预测标签的阈值化——更具重要性。这是因为在不可分离的离群点分数函数上确定最优阈值是一个不适定问题。然而，缺少预测标签不仅阻碍了当前离群点检测器的一些实际应用，还导致这些方法无法通过利用数据集的自监督得到增强。为改进现有打分方法，我们提出了多阈值化（Multi-T）模块。该模块生成两个阈值，从无标注目标数据集中分离出内点和离群点；其中离群点用于获得更优的特征表示，而内点则提供未受污染的流形。大量实验验证了Multi-T能够显著提升所提出的离群点打分方法的性能。此外，Multi-T还使一种朴素的基于距离的方法达到了当前最优水平。
英文摘要：
In the realm of unsupervised image outlier detection, assigning outlier scores holds greater significance than its subsequent task: thresholding for predicting labels. This is because determining the optimal threshold on non-separable outlier score functions is an ill-posed problem. However, the lack of predicted labels not only hiders some real applications of current outlier detectors but also causes these methods not to be enhanced by leveraging the dataset's self-supervision. To advance existing scoring methods, we propose a multiple thresholding (Multi-T) module. It generates two thresholds that isolate inliers and outliers from the unlabelled target dataset, whereas outliers are employed to obtain better feature representation while inliers provide an uncontaminated manifold. Extensive experiments verify that Multi-T can significantly improve proposed outlier scoring methods. Moreover, Multi-T contributes to a naive distance-based method being state-of-the-art.
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。
