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.

为了解决持续学习模型中的灾难性遗忘问题，我们提出了一种名为MGSER-SAM的新型基于记忆回放的算法，该算法专门设计用于增强持续学习模型的泛化能力。通过与经验回放框架ER和DER++进行结合，MGSER-SAM可以有效地处理当前任务和之前存储的记忆之间权重扰动方向冲突的复杂挑战。通过在多个基准测试上进行严格的实验分析，MGSER-SAM在所有三种持续学习场景中都表现出了比现有基准模型更好的性能。与基于记忆回放的基准模型ER和DER++相比，MGSER-SAM不仅将测试准确率提高了24.4％和17.6％，还在每个基准测试中实现了最小遗忘。

MGSER-SAM：以记忆为导向的软化经验回放与锐度感知优化，用于增强的持续学习