Dynamic Data Augmentation with Gating Networks for Time Series Recognition

Daisuke Oba, Shinnosuke Matsuo, Brian Kenji Iwana

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method is not equally effective for every dataset, you need to select an appropriate method carefully. We propose a neural network that dynamically selects the best combination of data augmentation methods using a Gating Network and a mutually beneficial feature consistency loss. The Gating Network is able to control how much of each data augmentation is used for the representation within the network. The feature consistency loss gives a constraint that augmented features from the same input pattern should be in similar. In the experiments, we demonstrate the effectiveness of the proposed method on the 12 largest time-series datasets from 2018 UCR Time Series Archive and reveal the relationships between the data augmentation methods through analysis of the proposed method.

Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3034-3040
Number of pages7
ISBN (Electronic)9781665490627
DOIs
Publication statusPublished - 2022
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: Aug 21 2022Aug 25 2022

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2022-August
ISSN (Print)1051-4651

Conference

Conference26th International Conference on Pattern Recognition, ICPR 2022
Country/TerritoryCanada
CityMontreal
Period8/21/228/25/22

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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