Published as a conference paper at ICLR 2018 META-LEARNING FOR SEMI-SUPERVISED FEW-SHOT CLASSIFICATION
In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained on episodes representing different classification problems, each with a small labeled training set and its corresponding test set. In this work, we advance this few-shot classification paradigm towards a scenario where unlabeled examples are also available within each episode. We consider two situations: one where all unlabeled examples are assumed to belong to the same set of classes as the labeled examples of the episode, as well as the more challenging situation where examples from other distractor classes are also provided. To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes. These models are trained in an end-to-end way on episodes, to learn to leverage the unlabeled examples successfully. We evaluate these methods on versions of the Omniglot and miniImageNet benchmarks, adapted to this new framework augmented with unlabeled examples. We also propose a new split of ImageNet, consisting of a large set of classes, with a hierarchical structure. Our experiments confirm that our Prototypical Networks can learn to improve their predictions due to unlabeled examples, much like a semi-supervised algorithm would.
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.