Feasibility of Transfer Learning: A Mathematical Framework

TL;DR

This paper develops a mathematical framework to analyze the feasibility of transfer learning, showing under certain conditions an optimal transfer procedure exists and providing insights into feature augmentation and domain adaptation.

Contribution

It introduces a formal mathematical framework for transfer learning and formulates the feasibility problem as an optimization task, advancing theoretical understanding of transfer learning's possibilities.

Findings

Optimal transfer learning procedures exist under certain conditions.

Feature augmentation can significantly impact model performance.

Feasibility of efficient feature transfer in image classification is demonstrated.

Abstract

Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. It has enjoyed numerous empirical successes and inspired a growing number of theoretical studies. This paper addresses the feasibility issue of transfer learning. It begins by establishing the necessary mathematical concepts and constructing a mathematical framework for transfer learning. It then identifies and formulates the three-step transfer learning procedure as an optimization problem, allowing for the resolution of the feasibility issue. Importantly, it demonstrates that under certain technical conditions, such as appropriate choice of loss functions and data sets, an optimal procedure for transfer learning exists. This study of the feasibility issue brings additional insights into various transfer learning problems. It sheds light on the impact of feature augmentation on model performance, explores potential extensions of domain adaptation, and examines the feasibility of efficient feature extractor transfer in image classification.