  Open-set recognition refers to the problem in which classes that were not
seen during training appear at inference time. This requires the ability to
identify instances of novel classes while maintaining discriminative capability
for closed-set classification. OpenMax was the first deep neural network-based
approach to address open-set recognition by calibrating the predictive scores
of a standard closed-set classification network. In this paper we present
MetaMax, a more effective post-processing technique that improves upon
contemporary methods by directly modeling class activation vectors. MetaMax
removes the need for computing class mean activation vectors (MAVs) and
distances between a query image and a class MAV as required in OpenMax.
Experimental results show that MetaMax outperforms OpenMax and is comparable in
performance to other state-of-the-art approaches.
