iDECODe：共形分布外检测的分布内等方差
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
摘要：
诸如深度神经网络（DNN）之类的机器学习方法尽管在不同领域取得了成功，但已知它们经常在其训练分布之外的输入上生成具有高置信度的不正确预测。在安全关键域中部署DNN需要检测分发外（OOD）数据，以便DNN可以避免对这些数据进行预测。最近已经开发了许多用于OOD检测的方法，但仍有改进的空间。我们提出了新的方法iDECODe，利用分布等方差共形OOD检测。它依赖于一种新的基础非一致性措施和一种新的聚合方法，用于感应共形异常检测框架，从而保证有界的误检率。我们通过在图像和音频数据集上的实验证明了iDECODe的有效性，获得了最先进的结果。我们还表明，iDECODe可以检测对抗性的例子。
英文摘要：
Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution. The deployment of DNNs in safety-critical domains requires detection of out-of-distribution (OOD) data so that DNNs can abstain from making predictions on those. A number of methods have been recently developed for OOD detection, but there is still room for improvement. We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. It relies on a novel base non-conformity measure and a new aggregation method, used in the inductive conformal anomaly detection framework, thereby guaranteeing a bounded false detection rate. We demonstrate the efficacy of iDECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that iDECODe can detect adversarial examples.
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。
