Class-Incremental Learning on Multivariate Time Series Via Shape-Aligned Temporal Distillation
Qiao ZhongZheng, Minghui Hu, Xudong Jiang, Ponnuthurai Nagaratnam Suganthan, Savitha Ramasamy
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.
BibTeX
@inproceedings{icassp2023_classincremental,
title = {Class-Incremental Learning on Multivariate Time Series Via Shape-Aligned Temporal Distillation},
author = {Qiao ZhongZheng and Minghui Hu and Xudong Jiang and Ponnuthurai Nagaratnam Suganthan and Savitha Ramasamy},
booktitle = {ICASSP 2023},
year = {2023}
}