提出新算法提升记忆模型抗噪和抗攻击能力
Robust Bidirectional Associative Memory via Regularization Inspired by the Subspace Rotation Algorithm
- 基于子空间旋转思想设计无梯度训练算法
- 结合正交权重与梯度对齐，使模型抗干扰能力提升
- 适用于高容量关联记忆系统，适合安全敏感场景
双向联想记忆（BAM）使用双向反向传播（B-BP）训练时，常因鲁棒性差、对噪声和对抗攻击敏感而表现不佳。为此，我们提出一种新型无梯度训练算法——双向子空间旋转算法（B-SRA），显著提升了BAM的鲁棒性和收敛性。通过大量实验，我们发现两个关键原则——正交权重矩阵（OWM）和梯度模式对齐（GPA）——是增强BAM鲁棒性的核心。受此启发，我们在B-BP中引入新的正则化策略，使模型对扰动和对抗攻击的抵抗力大幅增强。我们进一步通过消融实验比较不同训练策略，确定最优配置，并在多种攻击场景和记忆容量（50、100、200对关联）下评估性能。结果表明，同时集成OWM与GPA的SAME配置表现最强。整体而言，B-SRA及所提正则化策略显著提升了联想记忆的鲁棒性，为构建抗干扰神经架构开辟了新方向。
原文摘要 · Abstract (English)
Bidirectional Associative Memory (BAM) trained with Bidirectional Backpropagation (B-BP) often suffers from poor robustness and high sensitivity to noise and adversarial attacks. To address these issues, we propose a novel gradient-free training algorithm, the Bidirectional Subspace Rotation Algorithm (B-SRA), which significantly improves the robustness and convergence behavior of BAM. Through comprehensive experiments, we identify two key principles -- orthogonal weight matrices (OWM) and gradient-pattern alignment (GPA) -- as central to enhancing the robustness of BAM. Motivated by these findings, we introduce new regularization strategies into B-BP, resulting in models with greatly improved resistance to corruption and adversarial perturbations. We further conduct an ablation study across different training strategies to determine the most robust configuration and evaluate BAM's performance under a variety of attack scenarios and memory capacities, including 50, 100, and 200 associative pairs. Among all methods, the SAME configuration, which integrates both OWM and GPA, achieves the strongest resilience. Overall, our results demonstrate that B-SRA and the proposed regularization strategies lead to substantially more robust associative memories and open new directions for building resilient neural architectures.
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