[1]WANG Xin,GUO Xinyao,WEI Wei,et al.Metric learning algorithm with adversarial sample triples constraints[J].CAAI Transactions on Intelligent Systems,2021,16(1):30-37.[doi:10.11992/tis.202009050]
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Metric learning algorithm with adversarial sample triples constraints

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 1 Page number: 30-37 Column: 学术论文—机器学习 Public date: 2021-01-05
Title:
Metric learning algorithm with adversarial sample triples constraints
Author(s):
WANG Xin1;  GUO Xinyao1;  WEI Wei1; 2;  LIANG Jiye1; 2
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China
Keywords:
machine learning; metric learning; triplet constraints; adversarial training; Mahalanobis distance; sample perturbation; convex optimization; gradient descent
CLC:
TP181
DOI:
10.11992/tis.202009050
Abstract:
Most of the existing metric learning algorithms with triple constraints use prior knowledge to construct constraints, which restricts the performance of metric learning algorithms to a certain extent. To solve this problem, the metric learning algorithm with adversarial sample triple constraints, named ASTCML, is proposed based on the idea of sample perturbation in adversarial training, in which the adversarial sample is learned near the original sample to construct adversarial triple constraints. The metric learning model is constructed on the basis of adversarial triples and original triples constraints. Experimental results show that the proposed algorithm overcomes the effect of prior knowledge that is problematic for existing fixed constraint methods and improves the classification accuracy. This shows that distinguishing triple constraints that are more difficult to distinguish can improve the performance of the algorithm.
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