Attention to Warp: Deep Metric Learning for Multivariate Time Series

Shinnosuke Matsuo, Xiaomeng Wu, Gantugs Atarsaikhan, Akisato Kimura, Kunio Kashino, Brian Kenji Iwana, Seiichi Uchida

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

1 Citation (Scopus)


Deep time series metric learning is challenging due to the difficult trade-off between temporal invariance to nonlinear distortion and discriminative power in identifying non-matching sequences. This paper proposes a novel neural network-based approach for robust yet discriminative time series classification and verification. This approach adapts a parameterized attention model to time warping for greater and more adaptive temporal invariance. It is robust against not only local but also large global distortions, so that even matching pairs that do not satisfy the monotonicity, continuity, and boundary conditions can still be successfully identified. Learning of this model is further guided by dynamic time warping to impose temporal constraints for stabilized training and higher discriminative power. It can learn to augment the inter-class variation through warping, so that similar but different classes can be effectively distinguished. We experimentally demonstrate the superiority of the proposed approach over previous non-parametric and deep models by combining it with a deep online signature verification framework, after confirming its promising behavior in single-letter handwriting classification on the Unipen dataset.

Original languageEnglish
Title of host publicationDocument Analysis and Recognition - ICDAR 2021 - 16th International Conference, Proceedings
EditorsJosep Lladós, Daniel Lopresti, Seiichi Uchida
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030863333
Publication statusPublished - 2021
Event16th International Conference on Document Analysis and Recognition, ICDAR 2021 - Lausanne, Switzerland
Duration: Sept 5 2021Sept 10 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12823 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Document Analysis and Recognition, ICDAR 2021

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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