However,theygenerally lose performance inmore realistic scenarios like learning in a continual manner. In contrast, humans can incorporate their prior knowledge to learn new concepts efficiently without forgetting older ones. In this work, we leverage meta-learning to encourage the model to learn how to learn continually. Inspired by human concept learning, we develop agenerative classifier that efficiently uses data-drivenexperience tolearn newconcepts even from fewsamples while being immune to forgetting. Along with cognitiveand theoretical insights, extensiveexperiments onstandard benchmarks demonstrate the effectiveness of the proposed method.