用码分调制层缓解持续学习中的遗忘和隐私泄露问题
Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification
- 引入码分调制层防止模型在持续学习中遗忘旧任务
- 在不重放数据情况下保持识别准确率并抵御成员推理攻击
- 适合注重隐私与稳定性的人体步态识别系统
持续学习（CL）因其能在预训练模型中融入新知识并降低训练计算成本，被应用于生物特征识别系统。然而，对小规模数据集进行渐进微调会引发灾难性遗忘，并使模型易受成员推理攻击。本文评估了码分调制层（CDML）在持续学习策略下步态识别系统的有效性。所提方法在不重放数据的前提下，既保持了所有任务的识别准确率，又有效缓解了成员推理攻击。实验表明，该方法显著降低了重传带来的影响。
原文摘要 · Abstract (English)
Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge within a pre-trained model and to the possibility of reducing the computational cost of training. Unfortunately, such approaches pose new challenges both in terms of final accuracy and privacy guarantees since a progressive fine-tuning of the model on small subsets expose them to catastrophic forgetting and successful inference attacks. This paper evaluates the efficiency of code division modulation layers (CDML) on a gait identification system which has been trained following a continual learning policy. The proposed approach preserves accuracy on all the tasks while mitigating membership inference attacks at the same time. Moreover, the impact of retransmission is minimized since replaying data is not necessary.
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