most citedGradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning

4 citations · 4 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2022★ 4 cited

Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning

Lin Ding, Peng Liu, Wenfeng Shen +2

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning. It uses gradient descent to learn commonalities between various ta…