Venue: ICLR 2017 conference

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper presents an attention based recurrent approach to one-shot learning. It reports quite strong experimental results (surpassing human performance/HBPL) on the Omniglot dataset, which is somewhat surprising because it seems to make use of very standard neural network machinery. The authors also note that other have helped verify the results (did Soumith Chintala reproduce the results?) and do provide source code.

After reading this paper, I'm left a little perplexed as to where the big performance improvements are coming from as it seems to share a lot of the same components of previous work. If the author's could report result from a broader suite of experiments like in previous work (e.g matching networks), it would much more convincing. An ablation study would also help with understanding why this model does so well.
review_title: Strong experimental results, but somewhat unclear where the improvements are coming from

final_score: rating: 3: Clear rejection
review_content: This paper describes a method that estimates the similarity between a set of images by alternatively attend each image with a recurrent manner. The idea of the paper is interesting, which mimic the human's behavior. However, there are several cons of the paper:

1. The paper is now well written. There are too many 'TODO', 'CITE' in the final version of the paper, which indicates that the paper is submitted in a rush or the authors did not take much care about the paper. I think the paper is not suitable to be published with the current version.

2. The missing of the experimental results. The paper mentioned the LFW dataset. However, the paper did not provide the results on LFW dataset. (At least I did not find it in the version of Dec. 13th)

3. The experiments of Omniglot dataset are not sufficient. I suggest that the paper provides some illustrations about how the model the attend two images (e.g. the trajectory of attend).
review_title: The paper need more improvements to be accepted

final_score: rating: null
review_content: The results for ARC in the current version of the paper do not outperform some baselines.  It seems from the author comment that new results which do outperform will be updated soon.  Could you also provide insight and interpretation (e.g. qualitative examples) into where and how the proposed method outperforms the baselines?
review_title: discussion of results

final_score: rating: null
review_content: It is weird that the paper describe LFW dataset but do not provides the results on it.
review_title: what is the results on LFW dataset?

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper introduces an attention-based recurrent network that learns to compare images by attending iteratively back and forth between a pair of images. Experiments show state-of-the-art results on Omniglot, though a large part of the performance gain comes from when extracted convolutional features are used as input.

The paper is significantly improved from the original submission and reflects changes based on pre-review questions. However, while there was an attempt made to include more qualitative results e.g. Fig. 2, it is still relatively weak and could benefit from more examples and analysis. Also, why is the attention in Fig. 2 always attending over the full character?  Although it is zooming in, shouldn’t it attend to relevant parts of the character?  Attending to the full character on a solid background seems a trivial solution where it is then unclear where the large performance gains are coming from.

While the paper is much more polished now, it is still lacking in details in some respects, e.g. details of the convolutional feature extractor used that gives large performance gain.
review_title: experimental section improved but still very weak on analysis and insight

final_score: rating: null
review_content: In your paper, you write that you are missing results that will be reported.

Do you have a newer version of your paper? In the current version your model doesn't perform as well as other methods. Could you report your "full context arc" results? Adding other more qualitative contributions or explaining why comparison is that not important would be nice too. 
review_title: missing results

Meta-review: This paper shows some strong performance numbers, but I agree with the reviewers that it requires more analysis of where those gains come from. The model is very simple, which is a positive, but more studies such as ablation studies and other examples would help a lot.
Decision: Reject