Web: http://arxiv.org/abs/2112.13121

Jan. 31, 2022, 2:11 a.m. | Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo

cs.LG updates on arXiv.org arxiv.org

Recently, it has been observed that a transfer learning solution might be all
we need to solve many few-shot learning benchmarks -- thus raising important
questions about when and how meta-learning algorithms should be deployed. In
this paper, we seek to clarify these questions by proposing a novel metric --
the diversity coefficient -- to measure the diversity of tasks in a few-shot
learning benchmark. We hypothesize that the diversity coefficient of the
few-shot learning benchmark is predictive of whether …

arxiv diversity failure learning transfer learning

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