基于分位数的最大似然训练用于离群值检测
Quantile-based Maximum Likelihood Training for Outlier Detection
AI总结：
针对图像分类中离群值假阳性问题，提出基于分位数的最大似然目标，通过将归一化流拟合到预训练判别特征上学习内点分布，有效提升离群值检测性能并减少对负训练数据的依赖。
摘要：
判别式学习能有效预测图像分类中的真实对象类别。然而，它常对离群值产生假阳性，这在自动驾驶和视频监控等应用中引发关键问题。以往解决该挑战的尝试包括：通过对比学习利用真实离群值数据训练图像分类器，或合成离群值进行自监督学习。此外，在像素空间对内点进行无监督生成建模在离群值检测方面成效有限。在本工作中，我们提出一种基于分位数的最大似然目标，用于学习内点分布，以在推理阶段改善离群值分离。该方法将归一化流拟合到预训练判别特征上，并根据评估的对数似然检测离群值。实验评估表明了我们方法的有效性，其超越了最先进的无监督离群值检测方法的性能。与近期一种自监督离群值检测方法相比，结果也具竞争力。本研究减少了对采样良好的负训练数据的依赖，这对医学诊断或遥感等领域尤为重要。
英文摘要：
Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training image classifiers through contrastive learning using actual outlier data or synthesizing outliers for self-supervised learning. Furthermore, unsupervised generative modeling of inliers in pixel space has shown limited success for outlier detection. In this work, we introduce a quantile-based maximum likelihood objective for learning the inlier distribution to improve the outlier separation during inference. Our approach fits a normalizing flow to pre-trained discriminative features and detects the outliers according to the evaluated log-likelihood. The experimental evaluation demonstrates the effectiveness of our method as it surpasses the performance of the state-of-the-art unsupervised methods for outlier detection. The results are also competitive compared with a recent self-supervised approach for outlier detection. Our work allows to reduce dependency on well-sampled negative training data, which is especially important for domains like medical diagnostics or remote sensing.
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。
