标准差(Standard Deviation),在概率统计中最常使用作为统计分布程度(statistical dispersion)上的测量。标准差定义为方差的算术平方根,反映组内个体间的离散程度。测量到分布程度的结果,原则上具有两种性质:一个总量的标准差或一个随机变量的标准差,及一个子集合样品数的标准差之间,有所差别。标准差的观念是由卡尔·皮尔逊(Karl Pearson)引入到统计中。

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In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier. Previous works tend to apply a threshold either on the classification scores or the loss function to reject the inputs that violate the assumption. However, these methods cannot achieve the low False Positive Ratio (FPR) required in safety applications. The proposed method is a rejection option based on hypothesis testing with probabilistic networks. With probabilistic networks, it is possible to estimate the distribution of outcomes instead of a single output. By utilizing Z-test over the mean and standard deviation for each class, the proposed method can estimate the statistical significance of the network certainty and reject uncertain outputs. The proposed method was experimented on with different configurations of the COCO and CIFAR datasets. The performance of the proposed method is compared with the Softmax Response, which is a known top-performing method. It is shown that the proposed method can achieve a broader range of operation and cover a lower FPR than the alternative.

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