The ability to incrementally learn new classes is crucial to the development
of real-world artificial intelligence systems. In this paper, we focus on a
challenging but practical few-shot class-incremental learning (FSCIL) problem.
FSCIL requires CNN models to incrementally learn new classes from very few
labelled samples, without forgetting the previously learned ones. To address
this problem, we represent the knowledge using a neural gas (NG) network, which
can learn and preserve the topology of the feature manifold formed by different
classes. On this basis, we propose the TOpology-Preserving knowledge
InCrementer (TOPIC) framework. TOPIC mitigates the forgetting of the old
classes by stabilizing NG's topology and improves the representation learning
for few-shot new classes by growing and adapting NG to new training samples.
Comprehensive experimental results demonstrate that our proposed method
significantly outperforms other state-of-the-art class-incremental learning
methods on CIFAR100, miniImageNet, and CUB200 datasets.

该研究重点关注 CNN 模型在少数有标记样本的情况下如何对新类别进行增量式学习，并提出了基于神经气网络的知识表示和 TOpology-Preserving knowledge InCrementer (TOPIC) 框架来解决这一问题，实验结果表明该方法在几个数据集上优于当前最先进的类增量学习方法。