Understanding Few-Shot Learning: Measuring Task Relatedness and Adaptation Difficulty via Attributes

Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL by exploring two key questions: (1) How to quantify the relationship between \emph{ training} and \emph{novel} tasks? To answer the first question, we propose Task Attribute Distance (TAD) as a metric to quantify the task relatedness via attributes. Unlike other metrics, TAD is independent of models, making it applicable to different FSL models. To address the second question, we utilize TAD metric to establish a theoretical connection between task relatedness and task adaptation difficulty.