New objects are continuously emerging in the dynamically changing world and a real-world artificial intelligence system should be capable of continual and effectual adaptation to new emerging classes without forgetting old ones. In view of this, in this paper we tackle a challenging and practical continual learning scenario named few-shot class-incremental learning (FSCIL), in which labeled data are given for classes in a base session but very limited labeled instances are available for new incremental classes. To address this problem, we propose a novel and succinct approach by introducing deep dictionary learning which is a hybrid learning architecture that combines dictionary learning and visual representation learning to provide a better space for characterizing different classes. We simultaneously optimize the dictionary and the feature extraction backbone in the base session, while only finetune the dictionary in the incremental session for adaptation to novel classes, which can alleviate the forgetting on base classes compared to finetuning the entire model. To further facilitate future adaptation, we also incorporate multiple pseudo classes into the base session training so that certain space projected by dictionary can be reserved for future new concepts. The extensive experimental results on CIFAR100, miniImageNet and CUB200 validate the effectiveness of our approach compared to other SOTA methods.

翻译：现实世界中的新对象不断涌现，真实的人工智能系统需能在持续且有效地适应新类别的同时，避免遗忘旧知识。为此，本文针对名为“少样本类增量学习”（FSCIL）这一具有挑战性的实际持续学习场景展开研究——该场景中，基会话阶段各类别拥有充足标注数据，而后续新增类别仅提供极少标注样本。为解决此问题，我们提出一种新颖且简洁的方法：引入深度字典学习——一种融合字典学习与视觉表征学习的混合学习架构，旨在构建更优的类别区分空间。我们在基会话阶段同步优化字典与特征提取主干网络，而在增量会话阶段仅微调字典以适应新类别，此举相较于整体微调模型可有效缓解基类遗忘。为促进未来适应性，我们在基会话训练中融入多个人造类别，使字典投影空间可为未来新概念保留特定区域。在CIFAR100、miniImageNet及CUB200数据集上的广泛实验表明，相较其他最优方法，本方法具有显著有效性。