Despite advances in deep learning, artificial neural networks do not learn the same way as humans do. Today, neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on learnt tasks when tasks are presented one at a time -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 model consists of a dual memory architecture to emulate the complementary learning systems (hippocampus and the neocortex) in the human brain and maintains a consolidated long-term memory via generative replay of past experiences. We (i) substantiate our claim that replay should be generative, (ii) show the benefits of generative replay and dual memory via experiments, and (iii) demonstrate improved performance retention even for small models with low capacity. Our architecture displays many important characteristics of the human memory and provides insights on the connection between sleep and learning in humans.
This paper introduces a neural network architecture for continual learning. The model is inspired by current knowledge about long term memory consolidation mechanisms in humans. As a consequence, it uses:
-	One temporary memory storage (inspired by hippocampus) and a long term memory
-	A notion of memory replay, implemented by generative models (VAE), in order to simultaneously train the network on different tasks and avoid catastrophic forgetting of previously learnt tasks.
Overall, although the result are not very surprising, the approach is well justified and extensively tested. It provides some insights on the challenges and benefits of replay based memory consolidation.
Comments:
	
1-	The results are somewhat unsurprising: as we are able to learn generative models of each tasks, we can use them to train on all tasks at the same time, a beat algorithms that do not use this replay approach. 
2-	It is unclear whether the approach provides a benefit for a particular application: as the task information has to be available, training separate task-specific architectures or using classical multitask learning approaches would not suffer from catastrophic forgetting and perform better (I assume). 
3-	So the main benefit of the approach seems to point towards the direction of what possibly happens in real brains. It is interesting to see how authors address practical issues of training based on replay and it show two differences with real brains: 1/ what we know about episodic memory consolidation (the system modeled in this paper) is closer to unsupervised learning, as a consequence information such as task ID and dictionary for balancing samples would not be available, 2/ the cortex (long term memory) already learns during wakefulness, while in the proposed algorithm this procedure is restricted to replay-based learning during sleep.
4-	Due to these differences, I my view, this work avoids addressing directly the most critical and difficult issues of catastrophic forgetting, which relates more to finding optimal plasticity rules for the network in an unsupervised setting
5-	The writing could have been more concise and the authors could make an effort to stay closer to the recommended number of pages.