weakness (since unfortunately there are other confounding factors). Further, orthogonally to the accuracy results, it is an interesting finding if standard approaches indeed suffer from this and the proposed method provides a remedy. I would therefore focus on these qualitative results more, and explain in the main text (not just the appendix) exactly how those visualization are created, and show those results for various models. 2) Somewhat related to the previous point: Pure metric-based models like Prototypical Networks lack an explicit mechanism for adaptation to each task at hand and it therefore seems plausible that they indeed suffer from the identified issue. However, it is less clear whether (or to what extent) models that do perform task-specific adaptation run the same danger. Intuitively, it seems that task adaptation also constitutes a mechanism for modifying the embedding function so that it favours the identification of objects that are targets of the associated classification task. By task adaptation here Iâm referring either to gradient-based adaptation (as in MAML and variants) or amortized conditioning-based adaptation (as in TADAM for example). Therefore, it would be very interesting to empirically compare the proposed method to these other ones not only in terms of classification accuracy but also qualitatively via visualizations as in Figure 1 that show the areas of the image that a model focuses more for making classification decisions. 3) Suggestion for the transductive framework: In Equation 8, it might be useful to incorporate the unlabeled examples in a weighted fashion instead of trusting that every example whose confidence surpasses a manually-set threshold can safely contribute to the prototype of the class that it is predicted to belong to. Specifically, the contribution of an unlabeled example to the updated class prototype can be weighted by the cosine similarity between that unlabeled example and that prototype (normalized across classes) and maybe additionally by the confidence c_b^q. This might slightly relieve the need to find the perfect threshold, since even if it is not conservative enough, a query example will be prohibited by modifying a prototype too much. An example of this is in Ren et al. [1] when computing refined prototypes by including unlabeled examples. 4) It seems that the weakness that this method is addressing would be more prominent in images comprised of multiple objects, or cluttered scenes. It would be very interesting to compare this approach to previous ones on few-shot classification on such a dataset! 5) For more easily assessing the degree of apples-to-applesness of the various comparisons in the tables, it would be useful to note which of the listed methods use data augmentations (as until recently this was not common practice for few-shot classification), what architecture they use, and what objective (most are episodic only but I think TADAM also performs joint training as the proposed method). 6) Another difference between the proposed approach and previous Prototypical Network-like methods is that the distance comparisons that inform the classification decisions are done in a feature-wise manner in this work. Specifically, when comparing embeddings a and b, for each spatial location, the distance between the feature vectors of a and b at that location is computed. The final estimate of the distance between a and b is obtained by aggregating those feature-wise distance estimates over all spatial locations. In contrast, usually the output of the last embedding layer is reshaped into a single vector (of shape channels x height x width) and distance comparisons of examples are made by directly comparing these vectors. It would therefore be useful to perform another ablation where a standard Prototypical Network is modified to perform the same type of distance comparison as their method. 7) Similarly to how the proposed transductive method was applied to other models, it would be nice to see results where the proposed joint training is also applied to other models, since this is orthogonal to the choice of the meta-learner too. References [1] Meta-Learning for Semi-Supervised Few-shot Classification. Ren et al. ICLR 2018.
4) It seems that the weakness that this method is addressing would be more prominent in images comprised of multiple objects, or cluttered scenes. It would be very interesting to compare this approach to previous ones on few-shot classification on such a dataset!