How are tasks formally defined in the context of few-shot classification?

[PabloMessina]

First of all, congratz for the paper and code. The idea behind MAML is elegantly simple yet powerful. In fact the paper is very easy to read, except for (and this is my question about) the part where you guys talk about its concrete application in Classification (section 5.2). Specifically, I don't understand how tasks are formally defined in this particular use case. In fact, since MAML trains an optimal parameter initialization theta, this theta should be the starting point for the parameters of all tasks' models. This entails that the number of parameters of theta should be the same number of parameters used for all tasks' models. However, in the context of classification, the usual approach is to use a softmax layer with as many output units as classes, and it might happen that different classification tasks involve 1) different classes and/or 2) different NUMBER of classes. If two tasks have a different number of classes, the softmax layer in each network would be different and the number of parameters would be different too. Therefore, there CANNOT BE a general theta that fits the number of parameters of all possible tasks (unless you enforce that each task should have a constant number of classes defined beforehand, which would become a limitation). How do you guys deal with this? I hope my question was clear enough.