517 citations · 542 across the 6 of their papers we have counts for
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cs.LG2019★ 19 cited
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
Hae Beom Lee, Hayeon Lee, Donghyun Na +4
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the num…
cs.LG2019★ 5 cited
MxML: Mixture of Meta-Learners for Few-Shot Classification
Minseop Park, Jungtaek Kim, Saehoon Kim +2
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples. Most of…