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Open Set Domain Adaptation Using Optimal Transport

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Marwa Kechaou
Romain Herault
Mokhtar Z. Alaya
Gilles Gasso
Published in
Machine Learning and Knowledge Discovery in Databases
Publisher
Springer International Publishing

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Abstract

The chapter introduces a novel approach using Optimal Transport (OT) to tackle open set domain adaptation, where the goal is to adapt models from a source domain to a target domain with unknown classes. The method consists of two main steps: rejection of unknown class samples and adaptation to label shifts. The rejection step involves learning an optimal transport map and target marginal distribution to discard samples from unknown classes. The label shift correction step focuses on re-weighting source samples to match the class proportions between the source and target domains. The chapter provides a detailed explanation of the OT framework, including the dual problems and optimization algorithms for each step. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed method, showing consistent outperformance over state-of-the-art approaches in various scenarios. The chapter concludes by highlighting the potential to extend the framework for deep network learning in open set domain adaptation.
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previous chapter Unsupervised Multi-source Domain Adaptation for Regression
next chapter Revisiting Wedge Sampling for Budgeted Maximum Inner Product Search
Title
Open Set Domain Adaptation Using Optimal Transport
Authors
Marwa Kechaou
Romain Herault
Mokhtar Z. Alaya
Gilles Gasso
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-030-67657-5

Electronic ISBN: 978-3-030-67658-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-67658-2_24
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