#StackBounty: #meta-learning What does the term episode mean in meta-learning?

Bounty: 50

Recall in meta-learning we have a meta-set which is a data-set of data-sets:

$$ D_{meta-set} = { D_n }^N_{n=1} $$

where $D_n$ is data-set (or usually a task). Usually defined as a data sampled from a target function for regression or N classes for a classification task. Usually these individual data sets $D_n$ are split into a support set (train set) and a query set (test set).

I’ve seen the term episode used in meta-learning but it’s not been clear to me. There are two possible definitions:

  1. 1 episode means sampling 1 single data set $D_n$
  2. 1 episode means sampling M data-sets. i.e. sampling a batch of tasks

which one is it?


reference:


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