This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our study shows that existing methods severely suffer from catastrophic forgetting, a well-known problem in incremental learning, which is aggravated due to data scarcity and imbalance in the few-shot setting. Our analysis further suggests that to prevent catastrophic forgetting, actions need to be taken in the primitive stage -- the training of base classes instead of later few-shot learning sessions. Therefore, we propose to search for flat local minima of the base training objective function and then fine-tune the model parameters within the flat region on new tasks. In this way, the model can efficiently learn new classes while preserving the old ones. Comprehensive experimental results demonstrate that our approach outperforms all prior state-of-the-art methods and is very close to the approximate upper bound. The source code is available at https://github.com/moukamisama/F2M.

本研究考虑了增量式少样本学习，解决了现有方法中存在的遗忘问题，并提出在原始阶段搜索基础任务的优化解来维持模型的良好性能。实验结果表明，这一方法优于现有状态-of-the-art方法，接近近似的最优结果。

通过寻找平坦极小值克服渐进式少样本学习中的灾难性遗忘