PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning

• URL: http://arxiv.org/abs/2503.07153v1
• Date: Mon, 10 Mar 2025 10:27:21 GMT
• Title: PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning
• Authors: Yuanlong Wu, Mingxing Nie, Tao Zhu, Liming Chen, Huansheng Ning, Yaping Wan,
• Abstract summary: Class-incremental learning (CIL) for time series data faces challenges in balancing stability against catastrophic forgetting and plasticity for new knowledge acquisition.<n>We present the first exploration of PTM-based Time Series Class-Incremental Learning (TSCIL)
• Score: 7.784244204592032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-incremental learning (CIL) for time series data faces critical challenges in balancing stability against catastrophic forgetting and plasticity for new knowledge acquisition, particularly under real-world constraints where historical data access is restricted. While pre-trained models (PTMs) have shown promise in CIL for vision and NLP domains, their potential in time series class-incremental learning (TSCIL) remains underexplored due to the scarcity of large-scale time series pre-trained models. Prompted by the recent emergence of large-scale pre-trained models (PTMs) for time series data, we present the first exploration of PTM-based Time Series Class-Incremental Learning (TSCIL). Our approach leverages frozen PTM backbones coupled with incrementally tuning the shared adapter, preserving generalization capabilities while mitigating feature drift through knowledge distillation. Furthermore, we introduce a Feature Drift Compensation Network (DCN), designed with a novel two-stage training strategy to precisely model feature space transformations across incremental tasks. This allows for accurate projection of old class prototypes into the new feature space. By employing DCN-corrected prototypes, we effectively enhance the unified classifier retraining, mitigating model feature drift and alleviating catastrophic forgetting. Extensive experiments on five real-world datasets demonstrate state-of-the-art performance, with our method yielding final accuracy gains of 1.4%-6.1% across all datasets compared to existing PTM-based approaches. Our work establishes a new paradigm for TSCIL, providing insights into stability-plasticity optimization for continual learning systems.

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