references I have that might help:


reference: arxiv.org/pdf/1606.04080.pdf 3


reference: proceedings.mlr.press/v48/santoro16.pdf 1



this is the quote I find unreadable:
More specifically, let us define a task T as distribution over possible label sets L. Typically we consider T to uniformly weight all data sets of up to a few unique classes (e.g., 5), with a few examples per class (e.g., up to 5). In this case, a label set L sampled from a task T , L ∼ T , will typically have 5 to 25 examples.
To form an “episode” to compute gradients and update our model, we first sample L from T (e.g.,
L could be the label set {cats, dogs}). We then use L to sample the support set S and a batch B
(i.e., both S and B are labelled examples of cats and dogs). The Matching Net is then trained to
minimise the error predicting the labels in the batch B conditioned on the support set S. This is a
form of meta-learning since the training procedure explicitly learns to learn from a given support set
to minimise a loss over a batch.


references for Trist’s answer:
In our setup, a task, or episode, in-
volves the presentation of some dataset D = {dt}Tt=1 = T
{(xt,yt)}t=1. For classification, yt is the class label for an image xt, and for regression, yt is the value of a hid- den function for a vector with real-valued elements xt, or simply a real-valued number xt (here on, for consistency, xt will be used).