Enhancing deep learning models for time series classification via knowledge distillation

arXiv:2607.06796v1 Announce Type: new Abstract: Deep learning has achieved remarkable success in various domains including time series analysis, computer vision and natural language processing. However, high computational and memory demands of state-of-the-art architectures pose challenges for deployment in resource-limited environments. Knowledge Distillation (KD) addresses this by transferring knowledge from a large teacher model to a smaller, more efficient student model while maintaining competitive performance. In this work, we investigate the effectiveness of KD for Time Series Classific

Source: arXiv cs.LG — read the full report at the original publisher.

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