Out-of-distribution detection (OOD) deals with anomalous input to neural networks. In the past, specialized methods have been proposed to reject predictions on anomalous input. We use outlier detection algorithms to detect anomalous input as reliable as specialized methods from the field of OOD. No neural network adaptation is required; detection is based on the model's softmax score. Our approach works unsupervised with an Isolation Forest or with supervised classifiers such as a Gradient Boosting machine.

翻译：室外分配检测(OOD)处理神经网络的异常输入,过去曾建议采用专门方法拒绝对异常输入的预测,我们使用室外检测算法来探测异常输入,如同OOD领域的专门方法一样可靠。不需要对神经网络进行调整;检测以模型的软体分数为基础。我们的方法与隔离森林或受监督的分类师(如 " 梯级推进机 " )毫无监督地运作。