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On Hard Episodes in Meta-Learning
[article]
2021
arXiv
pre-print
Existing meta-learners primarily focus on improving the average task accuracy across multiple episodes. Different episodes, however, may vary in hardness and quality leading to a wide gap in the meta-learner's performance across episodes. Understanding this issue is particularly critical in industrial few-shot settings, where there is limited control over test episodes as they are typically uploaded by end-users. In this paper, we empirically analyse the behaviour of meta-learners on episodes
arXiv:2110.11190v1
fatcat:5bqqt4ndjzhezmyj7wq54bxkdy