长尾分布下的精细化分离类别学习用于分布外检测
Long-Tailed Out-of-Distribution Detection with Refined Separate Class Learning
AI总结：
针对长尾分布下OOD检测性能下降问题，提出精细化分离类别学习（RSCL），通过动态类别级温度调整和信息性离群点挖掘，提升OOD检测性能并改善分类准确率。
摘要：
分布外（OOD）检测对于部署稳健的机器学习模型至关重要。然而，当训练数据遵循长尾分布时，由于OOD样本与头部/尾部类别之间的混淆，模型准确检测OOD样本的能力会显著受损。为了区分OOD样本与头部和尾部类别，分离类别学习（SCL）方法作为一种有前景的解决方案应运而生，该方法分别进行头部特定和尾部特定的类别学习。为此，我们审视了现有SCL工作的局限性，并揭示OOD检测性能显著受到静态缩放温度值的使用以及无信息离群点的存在的影响。为缓解这些局限性，我们提出了一种名为精细化分离类别学习（RSCL）的新方法，该方法利用动态类别级温度调整来调节每个分布内类别的温度参数，并利用信息性离群点挖掘来根据离群点与头部和尾部类别的亲和性识别多样类型的离群点。大量实验表明，RSCL在实现优越的OOD检测性能的同时，还提高了分布内数据的分类准确率。
英文摘要：
Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models. However, when training data follows a long-tailed distribution, the model's ability to accurately detect OOD samples is significantly compromised, due to the confusion between OOD samples and head/tail classes. To distinguish OOD samples from both head and tail classes, the separate class learning (SCL) approach has emerged as a promising solution, which separately conduct head-specific and tail-specific class learning. To this end, we examine the limitations of existing works of SCL and reveal that the OOD detection performance is notably influenced by the use of static scaling temperature value and the presence of uninformative outliers. To mitigate these limitations, we propose a novel approach termed Refined Separate Class Learning (RSCL), which leverages dynamic class-wise temperature adjustment to modulate the temperature parameter for each in-distribution class and informative outlier mining to identify diverse types of outliers based on their affinity with head and tail classes. Extensive experiments demonstrate that RSCL achieves superior OOD detection performance while improving the classification accuracy on in-distribution data.
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。
