Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and
  Applications
Minyang Hu Hong Chang Zong Guo Bingpeng Ma Shiguan Shan Xilin Chen
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 delving into two key questions: (1) How to
quantify the relationship between \emph{training} and \emph{novel} tasks? (2)
How does the relationship affect the \emph{adaptation difficulty} on novel
tasks for different models? To answer the two questions, we introduce Task
Attribute Distance (TAD) built upon attributes as a metric to quantify the task
relatedness. Unlike many existing metrics, TAD is model-agnostic, making it
applicable to different FSL models. Then, we utilize TAD metric to establish a
theoretical connection between task relatedness and task adaptation difficulty.
By deriving the generalization error bound on a novel task, we discover how TAD
measures the adaptation difficulty on novel tasks for FSL models. To validate
our TAD metric and theoretical findings, we conduct experiments on three
benchmarks. Our experimental results confirm that TAD metric effectively
quantifies the task relatedness and reflects the adaptation difficulty on novel
tasks for various FSL methods, even if some of them do not learn attributes
explicitly or human-annotated attributes are not available. Finally, we present
two applications of the proposed TAD metric: data augmentation and test-time
intervention, which further verify its effectiveness and general applicability.
The source code is available at https://github.com/hu-my/TaskAttributeDistance.