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.

翻译：在一般的类增量学习中，研究者通常使用样本集作为工具来避免持续学习过程中的灾难性遗忘。同时，研究者也注意到类增量学习与Oracle训练之间的差异，并尝试进行修正。近年来，研究者开始利用预训练模型开发类增量学习算法，取得了显著成果。本文观察到，在类增量学习中，由各类中心引导的权重之间的稳态被破坏，这与灾难性遗忘显著相关。基于此，我们提出了一种克服遗忘的新方法。在某些情况下，仅需在内存中保留每个类别的单个样本单元进行回放，并应用简单的梯度约束，即可获得非常好的效果。实验结果表明，在预训练模型条件下，我们的方法能以极低的计算成本并仅使用交叉熵损失函数，实现具有竞争力的性能。