Abstract
In transfer learning, one seeks to transfer related information from source tasks with sufficient data to help with the
learning of target task with only limited data. In this paper,
we propose a novel Coupled End-to-end Transfer Learning
(CETL) framework, which mainly consists of two convolutional neural networks (source and target) that connect to a
shared decoder. A novel loss function, the coupled loss, is
used for CETL training. From a theoretical perspective, we
demonstrate the rationale of the coupled loss by establishing a learning bound for CETL. Moreover, we introduce the
generalized Fisher information to improve multi-task optimization in CETL. From a practical aspect, CETL provides
a unified and highly flexible solution for various learning
tasks such as domain adaption and knowledge distillation.
Empirical result shows the superior performance of CETL
on cross-domain and cross-task image classification