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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution


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.

ホスト出版物のタイトルDocument Analysis and Recognition - ICDAR 2021 - 16th International Conference, Proceedings
編集者Josep Lladós, Daniel Lopresti, Seiichi Uchida
出版社Springer Science and Business Media Deutschland GmbH
出版ステータス出版済み - 2021
イベント16th International Conference on Document Analysis and Recognition, ICDAR 2021 - Lausanne, スイス
継続期間: 9 5 20219 10 2021


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12823 LNCS


会議16th International Conference on Document Analysis and Recognition, ICDAR 2021

All Science Journal Classification (ASJC) codes

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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