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Informed pair selection for self-paced metric learning in Siamese neural networks.

Martin, Kyle; Wiratunga, Nirmalie; Massie, Stewart; Clos, Jérémie

Authors

Jérémie Clos



Contributors

Max Bramer
Editor

Miltos Petridis
Editor

Abstract

Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning in SNNs is not reliant on explicit class knowledge; instead they require knowledge about the relationship between pairs. Though often ignored, we have found that appropriate pair selection is crucial to maximising training efficiency, particularly in scenarios where examples are limited. In this paper, we study the role of informed pair selection and propose a 2-phased strategy of exploration and exploitation. Random sampling provides the needed coverage for exploration, while areas of uncertainty modeled by neighbourhood properties of the pairs drive exploitation. We adopt curriculum learning to organise the ordering of pairs at training time using similarity knowledge as a heuristic for pair sorting. The results of our experimental evaluation show that these strategies are key to optimising training.

Citation

MARTIN, K., WIRATUNGA, N., MASSIE, S. and CLOS, J. 2018. Informed pair selection for self-paced metric learning in Siamese neural networks. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-030-04191-5_3

Conference Name 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018)
Conference Location Cambridge, UK
Start Date Dec 11, 2018
End Date Dec 13, 2018
Acceptance Date Sep 3, 2018
Online Publication Date Nov 16, 2018
Publication Date Dec 31, 2018
Deposit Date Jan 21, 2019
Publicly Available Date Jan 21, 2019
Publisher Springer
Pages 34-49
Series Title Lecture notes in computer science
Series Number 11311
Series ISSN 0302-9743
Book Title Artificial intelligence XXXV
ISBN 9783030041908
DOI https://doi.org/10.1007/978-3-030-04191-5_3
Keywords Deep learning; Siamese neural networks; Active learning; Case based reasoning; Machine learning; Metric learning
Public URL http://hdl.handle.net/10059/3269

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