Paper:
Continuously trainable models should be able to learn from a stream of data over an undefined period of time. This becomes even more difficult in a strictly incremental context, where data access to previously seen categories is not possible. To that end, we propose making use of a conditional generative adversarial model where the generator is used as a memory module through neural masking to emulate neural plasticity in the human brain. This memory module is further associated with a dynamic capacity expansion mechanism. Taken together, this method facilitates a resource efficient capacity adaption to accommodate new tasks, while retaining previously attained knowledge. The proposed approach outperforms state-of-the-art algorithms on publicly available datasets, overcoming catastrophic forgetting.

Review:
The proposed method tackles class-incremental continual learning, where new categories are incrementally exposed to the network but a classifier across all categories must be learned. The proposed method seems to be essentially a combination of generative replay (e.g. Deep Generative Replay) with AC-GAN as the model and attention (HAT), along with a growing mechanism to support saturating capacity. Quantitative results are shown on MNIST and SVHN while some analysis is provided on CIFAR.
Pros
 + The method combines the existing works in a way that makes sense, specifically AC-GAN to support a single generator network with attention-based methods to prevent forgetting in the generator.
 + The method results in good performance, although see caveats below. 
 + Analysis of the evolution of mask values over time is interesting.
Cons
 
 - The method is very confusingly presented and requires both knowledge of HAT as well as more than one reading to understand. The fact that HAT-like masks are used for a generative replay approach is clear, but the actual mechanism of "growing capacity" is not made clear at all especially in the beginning of the paper. Further the contributions are not clear at all, since a large part of the detailed approach/equations relate to the masking which was taken from previous work. The authors should on the claimed contributions. Is it a combination of DGR and HAT with some capacity expansion?
 - It is not clear whether pushing the catastrophic forgetting problem into the generator is the best approach. Clearly, replaying data accurately from all tasks will work well, but why is it harder to guard against the generative forgetting problem than the discriminative one?
 - The approach also seems to add a lot of complexity and heuristics/hyper-parameters. It also adds capacity and it is not at all made clear whether the comparison is fair since no analysis on number of parameters are shown.
 - Relatedly, better baselines should be used; for example, if the memory used by the generative model is merely put to storing randomly chosen instances from the tasks, how will the results compare? Clearly storing instances bypasses the forgetting problem completely (as memory size approaches the dataset size it turns into the joint problem) and it's not clear how many instances are really needed per task, especially for these simpler problems. As such, I find it surprising that simply storing instances would do as poorly as stated in this paper which says cannot provide enough diversity.
 It also seems strange to say that storing instances "violates the strictly incremental setup" while generative models do not. Obviously there is a tradeoff in terms of memory usage, privacy, performance, etc. but since none of these methods currently achieve the best across all of these there is no reason to rule out any of the methods. Otherwise you are just defining the problem in a way that excludes other simple approaches which work.
 - There are several methodological issues: Why are CIFAR results not shown in a table as is done for the other dataset? How many times were the experiments run and what were the variances? How many parameters are used (since capacity can increase?) It is for example not clear that the comparison to joint training is fair, when stating: "Interestingly, DGM outperforms joint training on the MNIST dataset using the same architecture. This suggests that the strictly incremental training methodology indeed forced the network to learn better generalizations compared to what it would learn given all the data." Doesn't DGM grow the capacity, and therefore this isn't that surprising? This is true throughout; as stated before it is not clear how many parameters and how much memory these methods need, which makes it impossible to compare.
 Some other minor issues in the writing includes: 
   1) The introduction makes it seem the generative replay is new, without citing approaches such as DGR (which are cited in the related work). The initial narrative mixes prior works' contributions and this paper's contributions; the contributions of the paper itself should be made clear, 
   2) Using the word "task" in describing "joint training" of the generative, discriminative, and classification networks is very confusing (since "task" is used for the continual learning description too, 
   3) There is no legend for CIFAR; what do the colors represent?
   4) There are several typos/grammar issues e.g. "believed to occurs", "important parameters sections", "capacity that if efficiently allocated", etc.).
 In summary, the paper presents what seems like an effective strategy for continual learning, by combining some existing methods together, but does not make it precise what the contributions are and the methodology/analysis make it hard to determine if the comparisons are fair or not. More rigorous experiments and analysis is needed to make this a good ICLR paper.