27 citations · 31 across the 6 of their papers we have counts for
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cs.LG2021
How Sensitive are Meta-Learners to Dataset Imbalance?
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey +2
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot Learning (FSL) algorithms by exposure to batches of tasks sampled from a meta-dataset. However, the standard…
cs.LG2021★ 4 cited
Few-Shot Learning with Class Imbalance
Mateusz Ochal, Massimiliano Patacchiola, Amos Storkey +2
Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning (ML), which exposes models to batches of tasks sampled from a meta-dataset to mimic tasks seen during…