开放域OOD主要数据集、评价指标汇总in-distribution data (分布内训练数据集)、OOD test d

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OOD 数据集

in-distribution data (分布内训练数据集)

We use CIFAR-10 and CIFAR-100 [ Krizhevsky et al., 2009 ] datasets as in-distribution data. （IND）
Berkeley DeepDrive-100k
Youtube_vis
PASCAL VOC

OOD test dataset （分布外测试数据集）

We use the standard split, and train with WideResNet architecture [Zagoruyko and Komodakis, 2016] with depth 40. 

For the OOD test dataset, we use the following six datasets: 

Textures [Cimpoi et al., 2014], 

SVHN [Netzer et al., 2011],

Places365 [Zhou et al., 2017],

LSUN-Crop [Yu et al., 2015],

LSUN-Resize [Yu et al., 2015], 

iSUN [Xu et al., 2015].

实验对比示例

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ood 评价指标 : Evaluation metrics.

Evaluation metrics. We evaluate the performance of OOD detection by measuring the following metrics:

(1) the false positive rate (FPR95) of OOD examples when the true positive rate of in-distribution examples is 95%; (2) the area under the receiver operating characteristic curve (AUROC); (3) the area under the precision-call curve (AUPR).

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