PDF(970 KB)
Non-salient region erasure for time series augmentation
Pin LIU, Xiaohui GUO, Bin SHI, Rui WANG, Tianyu WO, Xudong LIU
PDF(970 KB)
PDF(970 KB)
Non-salient region erasure for time series augmentation
| [1] |
Olson M Wyner A J Berk R. Modern neural networks generalize on small data sets. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018, 3623– 3632
|
| [2] |
Wang J Wang Z Li J Wu J. Multilevel wavelet decomposition network for interpretable time series analysis. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 2437– 2446
|
| [3] |
Yang W Huang H Zhang Z Chen X Huang K Zhang S. Towards rich feature discovery with class activation maps augmentation for person re-identification. In: Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019, 1389– 1398
|
| [4] |
Wang J , Peng Z , Wang X , Li C , Wu J . Deep fuzzy cognitive maps for interpretable multivariate time series prediction. IEEE Transactions on Fuzzy Systems, 2021, 29( 9): 2647– 2660
|
| [5] |
Lee D Lee S Yu H. Learnable dynamic temporal pooling for time series classification. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, 8288– 8296
|
| [6] |
Chen N , Zhu J , Chen J , Chen T . Dropout training for SVMs with data augmentation. Frontiers of Computer Science, 2018, 12( 4): 694– 713
|
| [7] |
Forestier G Petitjean F Dau H A Webb G I Keogh E. Generating synthetic time series to augment sparse datasets. In: Proceedings of 2017 IEEE International Conference on Data Mining. 2017, 865– 870
|
| [8] |
Iwana B K Uchida S. Time series data augmentation for neural networks by time warping with a discriminative teacher. In: Proceedings of the 25th International Conference on Pattern Recognition. 2021, 3558– 3565
|
/
| 〈 |
|
〉 |
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