  The ability to learn more and more concepts over time from incrementally
arriving data is essential for the development of a life-long learning system.
However, deep neural networks often suffer from forgetting previously learned
concepts when continually learning new concepts, which is known as catastrophic
forgetting problem. The main reason for catastrophic forgetting is that the
past concept data is not available and neural weights are changed during
incrementally learning new concepts. In this paper, we propose a
pseudo-rehearsal based class incremental learning approach to make neural
networks capable of continually learning new concepts. We use a conditional
generative adversarial network to consolidate old concepts memory and recall
pseudo samples during learning new concepts and a balanced online memory recall
strategy is to maximally maintain old memories. And we design a comprehensible
incremental concept learning network as well as a concept contrastive loss to
alleviate the magnitude of neural weights change. We evaluate the proposed
approach on MNIST, Fashion-MNIST and SVHN datasets and compare with other
rehearsal based approaches. The extensive experiments demonstrate the
effectiveness of our approach.
