Continual Learning with Deep Generative Replay
Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of the hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model ("generator") and a task solving model ("solver"). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.
Quality:
The paper is technically sound. Extensive comparisons between the proposed approach and other baseline approaches are being made.
Clarity:
The paper is well-organized. However, insufficient details are provided on the architectures of the discriminator, generator and the used optimizer. 
I would be surprised if the cited papers refer to the hippocampus as a short-term memory system since the latter typically refers to working memroy. I assume they all refer to hippocampus as a system for long-term memory consolidation. I advice to adapt accordingly.
A bit more attention should be paid to English writing style:
primate brain’s the most apparent distinction 
inputs are coincided with 
Systems(CLS) 
Recent evidences 
networks(GANs) 
A branch of works 
line 114:  real and outputs 1/2 everywhere => unclear; please reformulate
line 124:  real-like samples => realistic; real-life (?)
in both function 
past copy of a self. 
Sequence 
of scholar models were 
favorable than other two methods 
can recovers real input space 
Old Tasks Performance 
good performances 
with in same setting employed in section 4.3.  
Originality:
The proposed approach to solve catastrophic forgetting is new (to my knowledge) yet also straightforward, replacing experience replay with a replay mechanism based on a generative model. The authors do show convincingly that their approach performs much better than other approaches, approximating exact replay. 
A worry is that the generative model essentially stores the experiences which would explain why its performance is almost identical to that of experience replay. I think the authors need to provide more insight into the how performance scales with data size and show that the generative model becomes more and more memory efficient as the number of processed training samples increases. In other words: the generative model only makes sense if its memory footprint is much lower than that of just storing the examples. I am sure this would be the case but it is important and insightful to make this explicit.
Significance:
The results are important given the current interest in life-long learning. While the idea is expected in the light of current advances, the authors give a convincing demonstration of the usefulness of this approach. If the authors can improve the manuscript based on suggested modifications then I believe the paper would be a valuable contribution.