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.

本论文提出一种鉴别器违反封闭世界假设的有效方法，该方法基于概率网络的假设检验拒绝了不确定的输出并实现了低误报率的要求。