In general class-incremental learning, researchers typically use sample sets as a tool to avoid catastrophic forgetting during continuous learning. At the same time, researchers have also noted the differences between class-incremental learning and Oracle training and have attempted to make corrections. In recent years, researchers have begun to develop class-incremental learning algorithms utilizing pre-trained models, achieving significant results. This paper observes that in class-incremental learning, the steady state among the weight guided by each class center is disrupted, which is significantly correlated with catastrophic forgetting. Based on this, we propose a new method to overcoming forgetting . In some cases, by retaining only a single sample unit of each class in memory for replay and applying simple gradient constraints, very good results can be achieved. Experimental results indicate that under the condition of pre-trained models, our method can achieve competitive performance with very low computational cost and by simply using the cross-entropy loss.

本研究解决了类增量学习中的灾难性遗忘问题，特别是在使用预训练模型的情况下。提出了一种新方法，通过仅保留每个类别的单个样本并应用简单的梯度约束，显著提高了学习性能。实验结果表明，所提方法在低计算成本下能达到竞争力的效果。

基于预训练模型的高效类增量学习重放方法