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Multidimensional Belief Quantification for Label-Efficient Meta-Learning
[article]
2022
arXiv
pre-print
Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples introduces uncertainty, and quantifying model confidence for few-shot predictions is essential for many critical domains. Furthermore, few-shot tasks used in meta training are usually sampled randomly from a task distribution for an iterative model update, leading to high labeling costs and computational
arXiv:2203.12768v1
fatcat:spzkdqejhfbwnijstgyachbybe