CLIPScope：利用贝叶斯评分增强零样本OOD检测
CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoring
AI总结：
CLIPScope是一种零样本OOD检测方法，通过贝叶斯后验归一化置信度并挖掘OOD类别，在多个基准上达到最先进性能。
摘要：
检测分布外（OOD）样本对于机器学习模型在现实世界中的安全部署至关重要。视觉语言基础模型的最新进展使其能够在不需要分布内（ID）图像的情况下检测OOD样本。然而，这些零样本方法往往性能不佳，因为它们在检测置信度评分中没有充分考虑ID类别的似然性。因此，我们引入了CLIPScope，一种零样本OOD检测方法，它通过类别似然性对样本的置信度得分进行归一化，类似于贝叶斯后验更新。此外，CLIPScope还包含了一种新颖的策略，从大型词汇数据库中挖掘OOD类别。它根据CLIP嵌入距离选择距离ID类别最远和最近的类别标签，以最大化对OOD样本的覆盖。我们进行了广泛的消融研究和实证评估，证明了CLIPScope在各种OOD检测基准上达到了最先进的性能。
英文摘要：
Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models. Recent advances in vision language foundation models have made them capable of detecting OOD samples without requiring in-distribution (ID) images. However, these zero-shot methods often underperform as they do not adequately consider ID class likelihoods in their detection confidence scoring. Hence, we introduce CLIPScope, a zero-shot OOD detection approach that normalizes the confidence score of a sample by class likelihoods, akin to a Bayesian posterior update. Furthermore, CLIPScope incorporates a novel strategy to mine OOD classes from a large lexical database. It selects class labels that are farthest and nearest to ID classes in terms of CLIP embedding distance to maximize coverage of OOD samples. We conduct extensive ablation studies and empirical evaluations, demonstrating state of the art performance of CLIPScope across various OOD detection benchmarks.
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。
