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Open-set recognition refers to the problem in which classes that were notseen during training appear at inference time. This requires the ability toidentify instances of novel classes while maintaining discriminative capabilityfor closed-set classification. OpenMax was the first deep neural network-basedapproach to address open-set recognition by calibrating the predictive scoresof a standard closed-set classification network. In this paper we presentMetaMax, a more effective post-processing technique that improves uponcontemporary methods by directly modeling class activation vectors. MetaMaxremoves the need for computing class mean activation vectors (MAVs) anddistances between a query image and a class MAV as required in OpenMax.Experimental results show that MetaMax outperforms OpenMax and is comparable inperformance to other state-of-the-art approaches.

MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration