Qiao, Z., Hu, M., Jiang, X., Suganthan, P. N., & Savitha, R. (2023). Class-Incremental Learning on Multivariate Time Series Via Shape-Aligned Temporal Distillation. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp49357.2023.10094960
Abstract:
Class-incremental learning (CIL) on multivariate time series (MTS) is an important yet understudied problem. Based on practical privacy-sensitive circumstances, we propose a novel distillation-based strategy using a single-headed classifier without saving historical samples. We propose to exploit Soft-Dynamic Time Warping (Soft-DTW) for knowledge distillation, which aligns the feature maps along the temporal dimension before calculating the discrepancy. Compared with Euclidean distance, Soft-DTW shows its advantages in overcoming catastrophic forgetting and balancing the stability-plasticity dilemma. We construct two novel MTS-CIL benchmarks for comprehensive experiments. Combined with a prototype augmentation strategy, our framework demonstrates significant superiority over other prominent exemplar-free algorithms.
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Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - CNRS@CREATE
Grant Reference no. : NA