  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.
