Deep neural networks suffer from the catastrophic forgetting problem in the field of continual learning (CL). To address this challenge, we propose MGSER-SAM, a novel memory replay-based algorithm specifically engineered to enhance the generalization capabilities of CL models. We first intergrate the SAM optimizer, a component designed for optimizing flatness, which seamlessly fits into well-known Experience Replay frameworks such as ER and DER++. Then, MGSER-SAM distinctively addresses the complex challenge of reconciling conflicts in weight perturbation directions between ongoing tasks and previously stored memories, which is underexplored in the SAM optimizer. This is effectively accomplished by the strategic integration of soft logits and the alignment of memory gradient directions, where the regularization terms facilitate the concurrent minimization of various training loss terms integral to the CL process. Through rigorous experimental analysis conducted across multiple benchmarks, MGSER-SAM has demonstrated a consistent ability to outperform existing baselines in all three CL scenarios. Comparing to the representative memory replay-based baselines ER and DER++, MGSER-SAM not only improves the testing accuracy by $24.4\%$ and $17.6\%$ respectively, but also achieves the lowest forgetting on each benchmark.

翻译：深度神经网络在持续学习领域中面临灾难性遗忘问题。为应对这一挑战，我们提出MGSER-SAM，一种新颖的基于记忆回放的算法，专门用于增强持续学习模型的泛化能力。首先，我们整合了SAM优化器，该组件专为优化平坦性设计，能够无缝融入ER和DER++等经典经验回放框架。其次，MGSER-SAM创新性地解决了当前任务与先前存储记忆之间权重扰动方向冲突的复杂难题（这是SAM优化器尚未充分探索的方向）。我们通过软逻辑值的策略性整合与记忆梯度方向对齐，有效实现了这一点，其中正则化项促进了持续学习过程中各类训练损失项的同步最小化。通过在多个基准数据集上的严格实验分析，MGSER-SAM在所有三种持续学习场景中均展现出持续超越现有基线的能力。与代表性记忆回放基线ER和DER++相比，MGSER-SAM不仅将测试准确率分别提升24.4%和17.6%，还在每个基准数据集上实现了最低的遗忘率。