Paper:
Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks, which aim to learn novel concepts from very few examples. The hallucination process, however, is still far from generating effective samples for learning. In this work, we investigate two important requirements for the hallucinator -(i) precision: the generated examples should lead to good classifier performance, and (ii) collaboration: both the hallucinator and the classification component need to be trained jointly. By integrating these requirements as novel loss functions into a general meta-learning with hallucination framework, our model-agnostic PrecisE Collaborative hAlluciNator (PECAN) facilitates data hallucination to improve the performance of new classification tasks. Extensive experiments demonstrate state-of-the-art performance on competitive miniImageNet and ImageNet based few-shot benchmarks in various scenarios.

Review:
This paper describes a method that builds upon the work of Wang et al. It meta-learns to hallucinate additional samples for few-shot learning for classification tasks. Their two main insights of this paper are to propose a soft-precision term which compares the classifiers' predictions for all classes other than the ground truth class for both a few-shot training set and the hallucinated set and b) to introduce the idea of applying direct early supervision in the feature space in which the hallucination is conducted in addition to in the classifier embedding space. This allows for stronger supervision and prevents the hallucinated samples from not being representative of the classes. The authors show small, but consistent improvement in performance on two benchmarks: ImageNet and miniImageNet with two different network architectures versus various state-of-the-art meta-learning algorithms with and without hallucination. The authors have adequately cited and reviewed the existing literature. They have also conducted many experiments (both in the main paper and in the supplementary material) to show the superior performance of their approach versus the existing ones. Furthermore their ablation studies both for the type of soft precision loss and for their various individual losses are quite nice and thorough. 
Overall the contribution of this paper is incremental over Wang et al and is mainly in the introduction of their new loss terms to regularize the hallucination process. This is clearly evident from Table 2 (comparing rows 1 and 3), where much of the performance gain is attained by including the l_learner^cls term versus the collaborative loss term (comparing row 1 and row 4).
Furthermore, I would like the author to answer the following two questions:
1. The authors claim that their method is general applicable to all meta-learning methods and can be combined with them. Yet, the meta learning methods that they apply it to: prototypical networks, prototypical matching networks and cosine classifiers are all metric-learning-based meta-learning techniques. I would like the authors to outline the procedure (and preferably also show experiments) for applying their proposed technique to meta-learning based techniques that do not involve learning a metric-embedding space and instead learn the learning procedure via nested optimization, e.g. MAML and its variants.
2. In Table 4, I would like to see the results of PNM w/G or in other words the results of (Wang et al, 2018)'s method in comparison to the authors' proposed method.
3. The authors make no attempt to solve the problem of hallucinating examples for regression tasks. This is fine as it is perhaps outside he scope of their current work. However, I would like the authors to fully qualify their claims everywhere in the paper and restrict the contribution of their work to classification tasks only.