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Improved deep metric learning with local neighborhood component analysis.
- Source :
-
Information Sciences . Dec2022, Vol. 617, p165-176. 12p. - Publication Year :
- 2022
-
Abstract
- Deep metric learning aims to learn a discriminative feature space in which features have larger intra-class similarities and smaller inter-class similarities. Most recent studies mainly focus on designing different loss functions or sampling strategies, while ignoring a crucial limitation caused by mini-batch training. We argue that existing mini-batch-based approaches do not explore the global structure similarities among samples in feature space. As a result, instances and their k -nearest neighbors may not be semantically consistent. To this end, we propose a method, dubbed Local Neighborhood Component Analysis (LNCA), to improve deep metric learning. Specifically, LNCA leverages a feature memory bank, storing the feature vectors of all instances, to estimate the global structure similarities and determine the k nearest neighbors of samples in the feature space. Further, in order to refine the local neighborhood components of samples, LNCA introduces a metric to attract the positive neighbors and repulse the negative neighbors simultaneously. LNCA is a plug-and-play module and can be integrated into a general DML framework. Experimental results show that it can boost the generalization performance of existing DML approaches significantly. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DEEP learning
*NEIGHBORHOODS
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 617
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 161014305
- Full Text :
- https://doi.org/10.1016/j.ins.2022.10.090