  When neural networks process images which do not resemble the distribution
seen during training, so called out-of-distribution images, they often make
wrong predictions, and do so too confidently. The capability to detect
out-of-distribution images is therefore crucial for many real-world
applications. We divide out-of-distribution detection between novelty detection
---images of classes which are not in the training set but are related to
those---, and anomaly detection ---images with classes which are unrelated to
the training set. By related we mean they contain the same type of objects,
like digits in MNIST and SVHN. Most existing work has focused on anomaly
detection, and has addressed this problem considering networks trained with the
cross-entropy loss. Differently from them, we propose to use metric learning
which does not have the drawback of the softmax layer (inherent to
cross-entropy methods), which forces the network to divide its prediction power
over the learned classes. We perform extensive experiments and evaluate both
novelty and anomaly detection, even in a relevant application such as traffic
sign recognition, obtaining comparable or better results than previous works.
