Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks. While this has been studied experimentally, there lacks a foundational description of the transfer learning problem that exposes what related tasks are, and how they can be exploited. In this work, we present a definition for relatedness between tasks and identify foliations as a mathematical framework to represent such relationships.
Cite
@article{arxiv.2008.00546,
title = {A Foliated View of Transfer Learning},
author = {Janith Petangoda and Nick A. M. Monk and Marc Peter Deisenroth},
journal= {arXiv preprint arXiv:2008.00546},
year = {2020}
}