  Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts
continually with only a few samples, which is prone to suffer the catastrophic
forgetting and overfitting problems. The inaccessibility of old classes and the
scarcity of the novel samples make it formidable to realize the trade-off
between retaining old knowledge and learning novel concepts. Inspired by that
different models memorize different knowledge when learning novel concepts, we
propose a Memorizing Complementation Network (MCNet) to ensemble multiple
models that complements the different memorized knowledge with each other in
novel tasks. Additionally, to update the model with few novel samples, we
develop a Prototype Smoothing Hard-mining Triplet (PSHT) loss to push the novel
samples away from not only each other in current task but also the old
distribution. Extensive experiments on three benchmark datasets, e.g.,
CIFAR100, miniImageNet and CUB200, have demonstrated the superiority of our
proposed method.
