Hi Alex, Thanks for taking the time to respond.
Before I respond let me share what I understand from the meta-learning papers I’ve read (which are a bunch in some detail at this point).
In meta-learning there is a meta-set which has the collection of data-sets to learn from (usually also called tasks). Each task/data-set in the meta-set is usually split into a train/support set and a test/query set (usually 5+15 examples afaik its common). Each of these tasks/data-set is built as following:

If its regression one samples a function from a family of similar function and creates the examples from an input range where y=f_i(x) for task i or data-set D_i. With 20 examples then split it.
If it’s classification it’s a N-way, K-shot task (+ K_eval shots). So one usually samples 5 classes from the fraction of classes used for meta-training and then from each class sample K+K_eval examples (usually 5+15=20 total).

Then the meta-set has a bunch of these tasks/data-sets.
From Trist’s response it seems that an episode is 1 data-set/task. Not a (meta) batch of data-sets.
I am trying to confirm if this is correct but he’s published a paper with Yoshua Bengio, so he’s likely a reliable source, but it’s weird to me cuz I thought your answer is the correct one. But Im unsure.

Quote 2:

tristandeleu 2  commented 2 days ago 2


Usually an episode means one single dataset  D_i . If you have a (meta-)batch of size 16, this means you have 16 episodes/datasets in your (meta-)batch.