Web: http://arxiv.org/abs/2201.11775

Jan. 31, 2022, 2:11 a.m. | Ramnath Kumar, Tristan Deleu, Yoshua Bengio

cs.LG updates on arXiv.org arxiv.org

Few-shot learning aims to learn representations that can tackle novel tasks
given a small number of examples. Recent studies show that task distribution
plays a vital role in the model's performance. Conventional wisdom is that task
diversity should improve the performance of meta-learning. In this work, we
find evidence to the contrary; we study different task distributions on a
myriad of models and datasets to evaluate the effect of task diversity on
meta-learning algorithms. For this experiment, we train on …

arxiv diversity learning meta meta-learning

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