Our goal in this paper is to exploit heteroscedastic temperature scaling as a calibration strategy for out of distribution (OOD) detection. Heteroscedasticity here refers to the fact that the optimal temperature parameter for each sample can be different, as opposed to conventional approaches that use the same value for the entire distribution. To enable this, we propose a new training strategy called anchoring that can estimate appropriate temperature values for each sample, leading to state-of-the-art OOD detection performance across several benchmarks. Using NTK theory, we show that this temperature function estimate is closely linked to the epistemic uncertainty of the classifier, which explains its behavior. In contrast to some of the best-performing OOD detection approaches, our method does not require exposure to additional outlier datasets, custom calibration objectives, or model ensembling. Through empirical studies with different OOD detection settings -- far OOD, near OOD, and semantically coherent OOD - we establish a highly effective OOD detection approach. Code and models can be accessed here -- https://github.com/rushilanirudh/AMP

本文目的在于利用异方差温度缩放作为一种校准策略来检测非分布数据。提出一种新的训练策略，称为锚定，可以估计每个样本的适当温度值，从而实现在几个基准测试中最先进的OOD检测性能。通过实验研究，我们建立了一种高度有效的OOD检测方法。

神经网络定位锚定实现的分布外检测