用于持续步态识别中对抗遗忘和推理的码分调制层
Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification
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中文总结 AI 辅助
研究持续学习在步态识别系统中的应用，提出码分调制层方法，在持续学习策略训练的步态识别系统中，该方法能保留任务准确性、减轻推理攻击，还能最小化重传影响。
中文摘要 AI 辅助
持续学习（CL）因其能在预训练模型中整合新知识并降低训练计算成本，已被应用于生物识别系统。然而，这种方法在最终准确性和隐私保证方面带来新挑战，因为在小子集上对模型进行渐进微调会使其面临灾难性遗忘和成功的推理攻击。本文评估了码分调制层（CDML）在遵循持续学习策略训练的步态识别系统上的效率。所提方法在保留所有任务准确性的同时减轻了成员推理攻击。此外，由于无需重放数据，重传的影响被最小化。
英文摘要
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.
发表机构
- Department of Information Engineering, University of Padova, Padova, Italy（信息工程系，帕多瓦大学，帕多瓦，意大利）
机构由 AI 辅助整理，请以论文原文为准。
