Reviews: Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes

Quality: The technical content of the paper is well motivated and the approach taken is interesting. However, a few things are worth mentioning. 1 - The classification parameters for a given class are generated independently from the other classes. This means that the classifier is more likely to act as a prototypical model than a discriminative one. 2 - In the adaptation network, the auto-regressive component is not technically motivated. The fact that it improves results just shows the lack of capacity in the FiLM network as a way to modulate the feature extractor parameters alone. Did you compare different ways of modulating the feature extractor parameters? 3 - z_G is computed using only the inputs from the query set, what about the labels? 4 - The statement " Allowing θ to adapt during the second phase violates the principle of "train as you test", i.e., when test tasks are encountered, θ will be fixed, so it is important to simulate this scenario during training " is technically false as within each meta-learning step θ will be fixed even when is not pretrained. Thus, the justification for the training procedure is a bit weak despite the comparison between the proposed approach and the classical one.