Venue: ICLR 2018 conference

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper is an extension of the “prototypical network” which will be published in NIPS 2017. The classical few-shot learning has been limited to using the unlabeled data, while this paper considers employing the unlabeled examples available to help train each episode. The paper solves a new semi-supervised situation, which is more close to the setting of the real world, with an extension of the prototype network.  Sufficient implementation detail and analysis on results.

However, this is definitely not the first work on semi-supervised formed few-shot learning. There are plenty of works on this topic [R1, R2, R3]. The authors are advised to do a thorough survey of the relevant works in Multimedia and computer vision community. 
 
Another concern is that the novelty. This work is highly incremental since it is an extension of existing prototypical networks by adding the way of leveraging the unlabeled data. 

The experiments are also not enough. Not only some other works such as [R1, R2, R3]; but also the other naïve baselines should also be compared, such as directly nearest neighbor classifier, logistic regression, and neural network in traditional supervised learning. Additionally, in the 5-shot non-distractor setting on tiered ImageNet, only the soft kmeans method gets a little bit advantage against the semi-supervised baseline, does it mean that these methods are not always powerful under different dataset?

[R1] “Videostory: A new multimedia embedding for few-example recognition and translation of events,” in ACM MM, 2014

[R2] “Transductive Multi-View Zero-Shot Learning”, IEEE TPAMI 2015

[R3] “Video2vec embeddings recognize events when examples are scarce,” IEEE TPAMI 2014

review_title: limited novelty

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper proposes to extend the Prototypical Network (NIPS17) to the semi-supervised setting with three possible 
strategies. One consists in self-labeling the unlabeled data and then updating the prototypes on the basis of the 
assigned pseudo-labels. Another is able to deal with the case of distractors i.e.  unlabeled samples not beloning to
any of the known categories. In practice this second solution is analogous to the first, but a general 'distractor' class
is added. Finally the third technique learns to weight the samples according to their distance to the original prototypes.

These strategies are evaluated in a particular semi-supervised transfer learning setting:  the models are first trained 
on some source categories with few labeled data and large unlabeled samples (this setting is derived by subselecting
multiple times a large dataset), then they are used on a final target task with again few labeled data and large 
unlabeled samples but beloning to a different set of categories.

+ the paper is well written, well organized and overall easy to read
+/-  this work builds largely on previous work. It introduces only some small technical novelty inspired by soft-k-means
clustering that anyway seems to be effective.
+ different aspect of the problem are analyzed by varying the number of disctractors and varying the level of
semantic relatedness between the source and the target sets

Few notes and questions
1) why for the omniglot experiment the table reports the error results? It would be better to present accuracy as for the other tables/experiments
2) I would suggest to use source and target instead of train and test -- these two last terms are confusing because
actually there is a training phase also at test time.
3) although the paper indicate that there are different other few-shot methods that could be applicable here, 
no other approach is considered besides the prothotipical network and its variants. An further external reference 
could be used to give an idea of what would be the experimental result at least in the supervised case.





review_title: extension of the Prototypical Network to semi-supervised setting

final_score: rating: 6: Marginally above acceptance threshold
review_content: In this paper, the authors studied the problem of semi-supervised few-shot classification, by extending the prototypical networks into the setting of semi-supervised learning with examples from distractor classes.  The studied problem is interesting, and the paper is well-written. Extensive experiments are performed to demonstrate the effectiveness of the proposed methods.  While the proposed method is a natural extension of the existing works (i.e., soft k-means and meta-learning).On top of that, It seems the authors have over-claimed their model capability at the first place as the proposed model cannot properly classify the distractor examples but just only consider them as a single class of outliers. Overall, I would like to vote for a weakly acceptance regarding this paper.
review_title: The studied problem is interesting, and the paper is well-written. While the proposed method is a natural extension of the existing works. 

Meta-review: The paper extends the earlier work on Prototypical networks to semi-supervised setting. Reviewers largely agree that the paper is well-written. There are some concerns on the incremental nature of the paper wrt to the novelty aspect but in the light of reported empirical results which show clear improvement over earlier work and given the importance of the topic, I recommend acceptance. 
Decision: Accept (Poster)