用自监督集成蒸馏提升动作识别的准确率与抗干扰能力
Ensemble Distribution Distillation for Self-Supervised Human Activity Recognition
- 通过自监督集成蒸馏融合多模型分布，利用无标签数据提升性能
- 在多个数据集上显著提高对抗扰动下的鲁棒性，且推理无需额外计算开销
- 适合追求高可靠性与低部署成本的动作识别场景
人体动作识别（HAR）随着深度学习技术的发展取得显著进展，但数据需求量大、模型可靠性与鲁棒性不足仍是挑战。本文在自监督学习框架中引入集成分布蒸馏（Ensemble Distribution Distillation, EDD），利用未标记数据与部分监督策略，有效提升了预测准确性，获得了更可靠的不确定性估计，并大幅增强对对抗扰动的鲁棒性。该方法在不增加推理计算复杂度的前提下，显著提升了真实场景下的系统可靠性。我们在多个公开数据集上进行了评估，验证了所提方法的有效性。主要贡献包括：构建了自监督EDD框架，设计了一种专用于HAR的数据增强技术，并实证证明了其在提升鲁棒性与可靠性方面的优势。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) has seen significant advancements with the adoption of deep learning techniques, yet challenges remain in terms of data requirements, reliability and robustness. This paper explores a novel application of Ensemble Distribution Distillation (EDD) within a self-supervised learning framework for HAR aimed at overcoming these challenges. By leveraging unlabeled data and a partially supervised training strategy, our approach yields an increase in predictive accuracy, robust estimates of uncertainty, and substantial increases in robustness against adversarial perturbation; thereby significantly improving reliability in real-world scenarios without increasing computational complexity at inference. We demonstrate this with an evaluation on several publicly available datasets. The contributions of this work include the development of a self-supervised EDD framework, an innovative data augmentation technique designed for HAR, and empirical validation of the proposed method's effectiveness in increasing robustness and reliability.
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