Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting and overfitting to new classes. Existing methods tend to freeze more parts of network components and finetune others with an extra memory during incremental sessions. These methods emphasize preserving prior knowledge to ensure proficiency in recognizing old classes, thereby mitigating catastrophic forgetting. Meanwhile, constraining fewer parameters can help in overcoming overfitting with the assistance of prior knowledge. Following previous methods, we retain more prior knowledge and propose a prior knowledge-infused neural network (PKI) to facilitate FSCIL. PKI consists of a backbone, an ensemble of projectors, a classifier, and an extra memory. In each incremental session, we build a new projector and add it to the ensemble. Subsequently, we finetune the new projector and the classifier jointly with other frozen network components, ensuring the rich prior knowledge is utilized effectively. By cascading projectors, PKI integrates prior knowledge accumulated from previous sessions and learns new knowledge flexibly, which helps to recognize old classes and efficiently learn new classes. Further, to reduce the resource consumption associated with keeping many projectors, we design two variants of the prior knowledge-infused neural network (PKIV-1 and PKIV-2) to trade off a balance between resource consumption and performance by reducing the number of projectors. Extensive experiments on three popular benchmarks demonstrate that our approach outperforms state-of-the-art methods.

翻译：少样本类增量学习（FSCIL）旨在模型仅利用少量新类样本进行持续适应，面临两个公认的挑战：灾难性遗忘和对新类的过拟合。现有方法倾向于在增量会话中冻结更多网络组件，并借助额外记忆库对其他部分进行微调。这些方法强调保留先验知识以确保对旧类的识别能力，从而缓解灾难性遗忘。同时，在先验知识的辅助下，约束更少的参数有助于克服过拟合。遵循先前方法，我们保留更多先验知识，并提出一种先验知识融合神经网络（PKI）以促进FSCIL。PKI由主干网络、投影器集成、分类器和额外记忆库组成。在每个增量会话中，我们构建一个新的投影器并将其加入集成。随后，我们联合微调新投影器和分类器，同时保持其他网络组件冻结，确保有效利用丰富的先验知识。通过级联投影器，PKI整合了从先前会话积累的先验知识并灵活学习新知识，这有助于识别旧类并高效学习新类。此外，为降低保留多个投影器带来的资源消耗，我们设计了先验知识融合神经网络的两个变体（PKIV-1和PKIV-2），通过减少投影器数量在资源消耗与性能之间取得平衡。在三个主流基准上的大量实验表明，我们的方法优于现有最先进方法。