Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many real applications, it is often desirable to have the flexibility of learning additional concepts, without re-training on the full training set. This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes; and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall performance of both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes. In each episode, we train a set of new weights to recognize novel classes until they converge, and we show that the technique of recurrent back-propagation can back-propagate through the optimization process and facilitate the learning of the attractor network regularizer. We demonstrate that the learned attractor network can recognize novel classes while remembering old classes without the need to review the original training set, outperforming baselines that do not rely on an iterative optimization process.
The paper addresses the incremental few-shot learning problem where a model starts with base network and then introduces the novel classes, building a connection between novel and base classes via an attention module.
Strengths:
+ clear writing. 
+ the experiments are compared with related work and the ablation studies can verify the effectiveness of the proposed (or "introduced" would be a precise term) recurrent BP.
Weakness:
- [Novelty]
The paper title is called attention attractor network, which shares very relevance to previous CVPR work (Gidaris & Komodakis, 2018). So the first thing I was looking for is the clear description of the difference between these two. Unfortunately, in related work, authors mention the CVPR work without stating the difference (last few lines in Section 2). As such, I don't see much novelty in the paper compared with previous work. Eqn. (7)-(10) explicitly describes the attention formula. What's the distinction from the CVPR work?
- [Motivation of the regularizer using Recurrent BP is not clear]
The use of recurrent BP is probably the most distinction from previous work. However, I don't see a clear description on why such a technique is necessary.
Starting from the first line in Section 3.3, "since there is no closed-form of the regularizer in Eqn (13)", E needs BPTT or the introduced recurrent BP. This part is simply a re-adaption of other algorithms. A very simple question is, how about use other regularizers to replace Eqn (13)? 
- [Some experiments missing]
The experiments section 4.6 uses a case of None and "best WD" to address some of my concerns. This is good. Does the "gamma random" indicates only E is used without the ||W||^2? why the best WD for one-shot is zero? This implies the model is best for applying no weight decay?
What's the effect of using the recurrent BP technique to the CVPR work? Is there some similar improvement? If yes, then the paper makes some contribution by the regularization. If not, what's the reason?
How about using the truncated BPTT with a larger T?
In general, I think the recurrent BP part should be the highlight of the paper and yet authors fail to spread such a spirit in the abstract or title. And there are some experiments missed as I mentioned above.