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When Does Self-supervision Improve Few-shot Learning?
[article]
2020
arXiv
pre-print
We investigate the role of self-supervised learning (SSL) in the context of few-shot learning. Although recent research has shown the benefits of SSL on large unlabeled datasets, its utility on small datasets is relatively unexplored. We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets. The improvements are greater when the training set is smaller or the task is more challenging.
arXiv:1910.03560v2
fatcat:wt4oebe5ejcptdgxfhpghbldty