To tackle the issues of catastrophic forgetting and overfitting in few-shot class-incremental learning (FSCIL), previous work has primarily concentrated on preserving the memory of old knowledge during the incremental phase. The role of pre-trained model in shaping the effectiveness of incremental learning is frequently underestimated in these studies. Therefore, to enhance the generalization ability of the pre-trained model, we propose Learning with Prior Knowledge (LwPK) by introducing nearly free prior knowledge from a few unlabeled data of subsequent incremental classes. We cluster unlabeled incremental class samples to produce pseudo-labels, then jointly train these with labeled base class samples, effectively allocating embedding space for both old and new class data. Experimental results indicate that LwPK effectively enhances the model resilience against catastrophic forgetting, with theoretical analysis based on empirical risk minimization and class distance measurement corroborating its operational principles. The source code of LwPK is publicly available at: \url{https://github.com/StevenJ308/LwPK}.

翻译：为了解决小样本类增量学习（FSCIL）中的灾难性遗忘和过拟合问题，以往的研究主要集中在增量阶段保留旧知识的记忆。这些研究常常低估了预训练模型对增量学习有效性的塑造作用。因此，为提升预训练模型的泛化能力，我们提出基于先验知识的学习方法（LwPK），通过引入来自后续增量类别中少量无标签数据的近乎零成本先验知识来实现。我们对无标签的增量类样本进行聚类以生成伪标签，随后将这些样本与有标签的基础类样本联合训练，从而有效为新旧类别数据分配嵌入空间。实验结果表明，LwPK能有效增强模型对抗灾难性遗忘的能力，基于经验风险最小化和类距离测量的理论分析进一步验证了其运作原理。LwPK的源代码已公开于：\url{https://github.com/StevenJ308/LwPK}。