  Unknown examples that are unseen during training often appear in real-world
computer vision tasks, and an intelligent self-learning system should be able
to 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 neural
networks (DNNs) lack a foundation for post recognition score analysis. In this
paper, we propose a DNN structure in which multiple one-vs-rest sigmoid
networks follow a convolutional neural network feature extractor. A one-vs-rest
network, which is composed of rectified linear unit activation functions for
the hidden layers and a single sigmoid target class output node, can maximize
the ability to learn information from nonmatch examples. Furthermore, the
network yields a sophisticated nonlinear features-to-output mapping that is
explainable in the feature space. By introducing extreme value theory-based
calibration techniques, the nonlinear and explainable mapping provides a
well-grounded class membership probability models. Our experiments show that
one-vs-rest networks can provide more informative hidden representations for
unknown examples than the commonly used SoftMax layer. In addition, the
proposed probability model outperformed the state-of-the art methods in open
set classification scenarios.
