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Self-supervised contrastive learning has become a key technique in deep learning, particularly in time series analysis, due to its ability to learn meaningful representations without explicit supervision.
R. B. Cleveland, W. S. Cleveland, J. E. McRae, I. Terpenning et al. , “Stl: A seasonal-trend decomposition,” J. Off. Stat , vol. 6, no. 1, pp. 3–73, 1990
1990
Earlier work this paper cites.
R. Bousseljot, D. Kreiseler, and A. Schnabel, “Nutzung der ekg-signaldatenbank cardiodat der ptb über das internet,” 1995
1995
Earlier work this paper cites.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” circulation , vol. 101, no. 23, pp. e215–e220, 2000
2000
Earlier work this paper cites.
M. Theodosiou, “Forecasting monthly and quarterly time series using stl decomposition,” International Journal of Forecasting , vol. 27, no. 4, pp. 1178–1195, 2011
2011
Earlier work this paper cites.
G. P. Tolstov, Fourier series . Courier Corporation, 2012
2012
Earlier work this paper cites.
J. Reyes-Ortiz, D. Anguita, A. Ghio, L. Oneto, and X. Parra, “Human Activity Recognition Using Smartphones,” UCI Machine Learning Repository, 2012, DOI: https://doi.org/10.24432/C54S4K
2012
Earlier work this paper cites.
C. Lessmeier, J. K. Kimotho, D. Zimmer, and W. Sextro, “Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification,” in PHM Society European Conference , vol. 3, no. 1, 2016
2016
Earlier work this paper cites.
H. A. Dau, E. Keogh, K. Kamgar, C.-C. M. Yeh, Y. Zhu, S. Gharghabi, C. A. Ratanamahatana, Yanping, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista, “The ucr time series classification archive,” October 2018
2018
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PMLR, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton, “Big self-supervised models are strong semi-supervised learners,” Advances in neural information processing systems , vol. 33, pp. 22 243–22 255, 2020
2020
Earlier work this paper cites.
P. H. Le-Khac, G. Healy, and A. F. Smeaton, “Contrastive representation learning: A framework and review,” Ieee Access , vol. 8, pp. 193 907–193 934, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
V. Verma, T. Luong, K. Kawaguchi, H. Pham, and Q. Le, “Towards domain-agnostic contrastive learning,” in International Conference on Machine Learning . PMLR, 2021, pp. 10 530–10 541
2021
Cited alongside, same era.
2021
Cited alongside, same era.
H. Tang, G. Zhao, Y. Wu, and X. Qian, “Multisample-based contrastive loss for top-k recommendation,” IEEE Transactions on Multimedia , vol. 25, pp. 339–351, 2021
2021
Cited alongside, same era.
P. Sarkar, S. Lobmaier, B. Fabre, D. González, A. Mueller, M. G. Frasch, M. C. Antonelli, and A. Etemad, “Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning,” Scientific reports , vol. 11, no. 1, pp. 1–10, 2021
2021
Cited alongside, same era.
J. Zhang and K. Ma, “Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 650–16 659
2022
Later among the works it cites.
R. Krishnan, P. Rajpurkar, and E. J. Topol, “Self-supervised learning in medicine and healthcare,” Nature Biomedical Engineering , vol. 6, no. 12, pp. 1346–1352, 2022
2022
Later among the works it cites.
W. Ågren, “The nt-xent loss upper bound,” arXiv preprint arXiv:2205.03169 , 2022
2022
Later among the works it cites.
T. Mehari and N. Strodthoff, “Self-supervised representation learning from 12-lead ecg data,” Computers in Biology and Medicine , vol. 141, p. 105114, 2022
2022
Later among the works it cites.
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J. Han, X. Gu, and B. Lo, “Semi-supervised contrastive learning for generalizable motor imagery eeg classification,” in 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN) . IEEE, 2021, pp. 1–4
2021
Cited alongside, same era.
X. Jiang, J. Zhao, B. Du, and Z. Yuan, “Self-supervised contrastive learning for eeg-based sleep staging,” in 2021 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2021, pp. 1–8
2021
Cited alongside, same era.
H. Chen, G. Wang, G. Zhang, P. Zhang, and H. Yang, “Clecg: A novel contrastive learning framework for electrocardiogram arrhythmia classification,” IEEE Signal Processing Letters , vol. 28, pp. 1993–1997, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. Yang, Z. Zhang, and R. Cui, “Timeclr: A self-supervised contrastive learning framework for univariate time series representation,” Knowledge-Based Systems , vol. 245, p. 108606, 2022
2022
Cited alongside, same era.
K. Wickstrøm, M. Kampffmeyer, K. Ø. Mikalsen, and R. Jenssen, “Mixing up contrastive learning: Self-supervised representation learning for time series,” Pattern Recognition Letters , vol. 155, pp. 54–61, 2022
2022
Cited alongside, same era.
X. Zhang, Z. Zhao, T. Tsiligkaridis, and M. Zitnik, “Self-supervised contrastive pre-training for time series via time-frequency consistency,” Advances in Neural Information Processing Systems , vol. 35, pp. 3988–4003, 2022
2022
Cited alongside, same era.
J. Pöppelbaum, G. S. Chadha, and A. Schwung, “Contrastive learning based self-supervised time-series analysis,” Applied Soft Computing , vol. 117, p. 108397, 2022
2022
Cited alongside, same era.
Z. Liu, A. Alavi, M. Li, and X. Zhang, “Self-supervised contrastive learning for medical time series: A systematic review,” Sensors , vol. 23, no. 9, p. 4221, 2023
2023
Later among the works it cites.
A. Miyai, Q. Yu, D. Ikami, G. Irie, and K. Aizawa, “Rethinking rotation in self-supervised contrastive learning: Adaptive positive or negative data augmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 2809–2818
2023
Later among the works it cites.
D. Luo, W. Cheng, Y. Wang, D. Xu, J. Ni, W. Yu, X. Zhang, Y. Liu, Y. Chen, H. Chen et al. , “Time series contrastive learning with information-aware augmentations,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, 2023, pp. 4534–4542
2023
Later among the works it cites.
2023
Later among the works it cites.
K. Zhang, Q. Wen, C. Zhang, R. Cai, M. Jin, Y. Liu, J. Y. Zhang, Y. Liang, G. Pang, D. Song et al. , “Self-supervised learning for time series analysis: Taxonomy, progress, and prospects,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Closest in time.
B. U. Demirel and C. Holz, “Finding order in chaos: A novel data augmentation method for time series in contrastive learning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
Y. Wang, Y. Han, H. Wang, and X. Zhang, “Contrast everything: A hierarchical contrastive framework for medical time-series,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.