  Despite advances in deep learning, neural networks can only learn multiple
tasks when trained on them jointly. When tasks arrive sequentially, they lose
performance on previously learnt tasks. This phenomenon called catastrophic
forgetting is a fundamental challenge to overcome before neural networks can
learn continually from incoming data. In this work, we derive inspiration from
human memory to develop an architecture capable of learning continuously from
sequentially incoming tasks, while averting catastrophic forgetting.
Specifically, our contributions are: (i) a dual memory architecture emulating
the complementary learning systems (hippocampus and the neocortex) in the human
brain, (ii) memory consolidation via generative replay of past experiences,
(iii) demonstrating advantages of generative replay and dual memories via
experiments, and (iv) improved performance retention on challenging tasks even
for low capacity models. Our architecture displays many characteristics of the
mammalian memory and provides insights on the connection between sleep and
learning.
