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Unknown examples that are unseen during training often appear in real-worldcomputer vision tasks, and an intelligent self-learning system should be ableto differentiate between known and unknown examples. Open set recognition,which addresses this problem, has been studied for approximately a decade.However, conventional open set recognition methods based on deep neuralnetworks (DNNs) lack a foundation for post recognition score analysis. In thispaper, we propose a DNN structure in which multiple one-vs-rest sigmoidnetworks follow a convolutional neural network feature extractor. A one-vs-restnetwork, which is composed of rectified linear unit activation functions forthe hidden layers and a single sigmoid target class output node, can maximizethe ability to learn information from nonmatch examples. Furthermore, thenetwork yields a sophisticated nonlinear features-to-output mapping that isexplainable in the feature space. By introducing extreme value theory-basedcalibration techniques, the nonlinear and explainable mapping provides awell-grounded class membership probability models. Our experiments show thatone-vs-rest networks can provide more informative hidden representations forunknown examples than the commonly used SoftMax layer. In addition, theproposed probability model outperformed the state-of-the art methods in openset classification scenarios.

One-vs-Rest Network-based Deep Probability Model for Open Set Recognition