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Large-Scale Meta-Learning with Continual Trajectory Shifting
[article]
2022
arXiv
pre-print
Meta-learning of shared initialization parameters has shown to be highly effective in solving few-shot learning tasks. However, extending the framework to many-shot scenarios, which may further enhance its practicality, has been relatively overlooked due to the technical difficulties of meta-learning over long chains of inner-gradient steps. In this paper, we first show that allowing the meta-learners to take a larger number of inner gradient steps better captures the structure of heterogeneous
arXiv:2102.07215v3
fatcat:caq72vxclrbrlnodxdsxsf55hq