  In open set recognition, deep neural networks encounter object classes that
were unknown during training. Existing open set classifiers distinguish between
known and unknown classes by measuring distance in a network's logit space,
assuming that known classes cluster closer to the training data than unknown
classes. However, this approach is applied post-hoc to networks trained with
cross-entropy loss, which does not guarantee this clustering behaviour. To
overcome this limitation, we introduce the Class Anchor Clustering (CAC) loss.
CAC is a distance-based loss that explicitly trains known classes to form tight
clusters around anchored class-dependent centres in the logit space. We show
that training with CAC achieves state-of-the-art performance for distance-based
open set classifiers on all six standard benchmark datasets, with a 15.2% AUROC
increase on the challenging TinyImageNet, without sacrificing classification
accuracy. We also show that our anchored class centres achieve higher open set
performance than learnt class centres, particularly on object-based datasets
and large numbers of training classes.
