Spatial Location Constraint Prototype Loss for Open Set Recognition
- URL: http://arxiv.org/abs/2110.11013v2
- Date: Mon, 25 Oct 2021 02:30:46 GMT
- Title: Spatial Location Constraint Prototype Loss for Open Set Recognition
- Authors: Ziheng Xia, Ganggang Dong, Penghui Wang, Hongwei Liu
- Abstract summary: How to reduce the open space risk is the key of open set recognition.
This paper explores the origin of the open space risk by analyzing the distribution of known and unknown classes features.
- Score: 17.725082940096257
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One of the challenges in pattern recognition is open set recognition.
Compared with closed set recognition, open set recognition needs to reduce not
only the empirical risk, but also the open space risk, and the reduction of
these two risks corresponds to classifying the known classes and identifying
the unknown classes respectively. How to reduce the open space risk is the key
of open set recognition. This paper explores the origin of the open space risk
by analyzing the distribution of known and unknown classes features. On this
basis, the spatial location constraint prototype loss function is proposed to
reduce the two risks simultaneously. Extensive experiments on multiple
benchmark datasets and many visualization results indicate that our methods is
superior to most existing approaches.
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