Challenging the Notion of Task Diversity in Meta-Learning

There is no universal task sampling strategy for optimal meta-learning performance, as over-constraining task diversity can lead to under-fitting or over-fitting. The generalization ability of meta-learning models is influenced by task diversity, entropy, and difficulty.