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

CVPR 2018 Shixing ChenCaojin ZhangMing Dong

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... (read more)

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