Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Abstract
Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.
Cite
@article{arxiv.1605.06391,
title = {Deep Multi-task Representation Learning: A Tensor Factorisation Approach},
author = {Yongxin Yang and Timothy Hospedales},
journal= {arXiv preprint arXiv:1605.06391},
year = {2017}
}
Comments
9 pages, Accepted to ICLR 2017 Conference Track. This is a conference version of the paper. For the multi-domain learning part (not in this version), please refer to https://arxiv.org/pdf/1605.06391v1.pdf