  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. Similarly, it was shown that feature extraction
models in combination with outlier detection algorithms are well suited to
detect 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 using an Isolation Forest and can be
further improved by using a supervised learning method such as Gradient
Boosting.
