  This paper presents a practical and simple yet efficient method to
effectively deal with the catastrophic forgetting for Class Incremental
Learning (CIL) tasks. CIL tends to learn new concepts perfectly, but not at the
expense of performance and accuracy for old data. Learning new knowledge in the
absence of data instances from previous classes or even imbalance samples of
both old and new classes makes CIL an ongoing challenging problem. These issues
can be tackled by storing exemplars belonging to the previous tasks or by
utilizing the rehearsal strategy. Inspired by the rehearsal strategy with the
approach of using generative models, we propose ClaRe, an efficient solution
for CIL by remembering the representations of learned classes in each
increment. Taking this approach leads to generating instances with the same
distribution of the learned classes. Hence, our model is somehow retrained from
the scratch using a new training set including both new and the generated
samples. Subsequently, the imbalance data problem is also solved. ClaRe has a
better generalization than prior methods thanks to producing diverse instances
from the distribution of previously learned classes. We comprehensively
evaluate ClaRe on the MNIST benchmark. Results show a very low degradation on
accuracy against facing new knowledge over time. Furthermore, contrary to the
most proposed solutions, the memory limitation is not problematic any longer
which is considered as a consequential issue in this research area.
