  Few-shot class-incremental learning (FSCIL), which targets at continuously
expanding model's representation capacity under few supervisions, is an
important yet challenging problem. On the one hand, when fitting new tasks
(novel classes), features trained on old tasks (old classes) could
significantly drift, causing catastrophic forgetting. On the other hand,
training the large amount of model parameters with few-shot novel-class
examples leads to model over-fitting. In this paper, we propose a learnable
expansion-and-compression network (LEC-Net), with the aim to simultaneously
solve catastrophic forgetting and model over-fitting problems in a unified
framework. By tentatively expanding network nodes, LEC-Net enlarges the
representation capacity of features, alleviating feature drift of old network
from the perspective of model regularization. By compressing the expanded
network nodes, LEC-Net purses minimal increase of model parameters, alleviating
over-fitting of the expanded network from a perspective of compact
representation. Experiments on the CUB/CIFAR-100 datasets show that LEC-Net
improves the baseline by 5~7% while outperforms the state-of-the-art by 5~6%.
LEC-Net also demonstrates the potential to be a general incremental learning
approach with dynamic model expansion capability.
