  We address the problem of class incremental learning, which is a core step
towards achieving adaptive vision intelligence. In particular, we consider the
task setting of incremental learning with limited memory and aim to achieve
better stability-plasticity trade-off. To this end, we propose a novel
two-stage learning approach that utilizes a dynamically expandable
representation for more effective incremental concept modeling. Specifically,
at each incremental step, we freeze the previously learned representation and
augment it with additional feature dimensions from a new learnable feature
extractor. This enables us to integrate new visual concepts with retaining
learned knowledge. We dynamically expand the representation according to the
complexity of novel concepts by introducing a channel-level mask-based pruning
strategy. Moreover, we introduce an auxiliary loss to encourage the model to
learn diverse and discriminate features for novel concepts. We conduct
extensive experiments on the three class incremental learning benchmarks and
our method consistently outperforms other methods with a large margin.
