TLXML: Task-Level Explanation of Meta-Learning via Influence Functions
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
The scheme of adaptation via meta-learning is seen as an ingredient for
solving the problem of data shortage or distribution shift in real-world
applications, but it also brings the new risk of inappropriate updates of the
model in the user environment, which increases the demand for explainability.
Among the various types of XAI methods, establishing a method of explanation
based on past experience in meta-learning requires special consideration due to
its bi-level structure of training, which has been left unexplored. In this
work, we propose influence functions for explaining meta-learning that measure
the sensitivities of training tasks to adaptation and inference. We also argue
that the approximation of the Hessian using the Gauss-Newton matrix resolves
computational barriers peculiar to meta-learning. We demonstrate the adequacy
of the method through experiments on task distinction and task distribution
distinction using image classification tasks with MAML and Prototypical
Network.
