Adversarial Motorial Prototype Framework for Open Set Recognition

• URL: http://arxiv.org/abs/2108.04225v1
• Date: Tue, 13 Jul 2021 07:31:34 GMT
• Title: Adversarial Motorial Prototype Framework for Open Set Recognition
• Authors: Ziheng Xia, Penghui Wang, Ganggang Dong, and Hongwei Liu
• Abstract summary: Open set recognition is designed to identify known classes and to reject unknown classes simultaneously. This paper proposes the motorial prototype framework (MPF) which classifies known classes according to the prototype classification idea. Second, this paper proposes the adversarial motorial prototype framework (AMPF) based on the MPF. Third, this paper proposes an upgraded version of the AMPF, AMPF++, which adds much more generated unknown samples into the training phase.
• Score: 16.22539914400299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open set recognition is designed to identify known classes and to reject unknown classes simultaneously. Specifically, identifying known classes and rejecting unknown classes correspond to reducing the empirical risk and the open space risk, respectively. First, the motorial prototype framework (MPF) is proposed, which classifies known classes according to the prototype classification idea. Moreover, a motorial margin constraint term is added into the loss function of the MPF, which can further improve the clustering compactness of known classes in the feature space to reduce both risks. Second, this paper proposes the adversarial motorial prototype framework (AMPF) based on the MPF. On the one hand, this model can generate adversarial samples and add these samples into the training phase; on the other hand, it can further improve the differential mapping ability of the model to known and unknown classes with the adversarial motion of the margin constraint radius. Finally, this paper proposes an upgraded version of the AMPF, AMPF++, which adds much more generated unknown samples into the training phase. In this paper, a large number of experiments prove that the performance of the proposed models is superior to that of other current works.

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