Coupled End-to-End Transfer Learning with Generalized Fisher Information

By Shixing Chen, Caojin Zhang, Ming Dong. 2018

30 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Ocean Engineering

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In Artificial Intelligence

Citations in the corpus

Variational Information Distillation for Knowledge Transfer (2019, methodology): “Besides the knowledge transfer methods based on mean squared error matching, several works [6, 13, 30, 31] have proposed indirectly matching the handcrafted features extracted from intermediate layers.”
Online Knowledge Distillation with Diverse Peers (2019, methodology): “In (Zhang et al. 2018), each student model learns from the simple average of predictions of all other group members and requires complex asynchronous updating among different networks.”