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

• URL: http://arxiv.org/abs/2004.08067v2
• Date: Thu, 28 May 2020 06:37:47 GMT
• Title: One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
• Authors: Jaeyeon Jang and Chang Ouk Kim
• Abstract summary: An intelligent self-learning system should be able to differentiate between known and unknown examples. One-vs-rest networks can provide more informative hidden representations for unknown examples than the commonly used SoftMax layer. The proposed probability model outperformed the state-of-the art methods in open set classification scenarios.
• Score: 6.85316573653194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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