%0 Generic %1 long2015learning %A Long, Mingsheng %A Cao, Zhangjie %A Wang, Jianmin %A Yu, Philip S. %D 2015 %K deep_learning multitask %T Learning Multiple Tasks with Multilinear Relationship Networks %U http://arxiv.org/abs/1506.02117 %X Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers. This paper presents Multilinear Relationship Networks (MRN) that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets.

@misc{long2015learning, abstract = {Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers. This paper presents Multilinear Relationship Networks (MRN) that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets.}, added-at = {2018-04-27T11:39:46.000+0200}, author = {Long, Mingsheng and Cao, Zhangjie and Wang, Jianmin and Yu, Philip S.}, biburl = {https://www.bibsonomy.org/bibtex/2a956a6866e705126a2b6e8db1d45f314/dallmann}, description = {Learning Multiple Tasks with Multilinear Relationship Networks}, interhash = {2008d095f2c996b504395a12a2b74d71}, intrahash = {a956a6866e705126a2b6e8db1d45f314}, keywords = {deep_learning multitask}, note = {cite arxiv:1506.02117Comment: NIPS 2017}, timestamp = {2018-04-27T11:39:46.000+0200}, title = {Learning Multiple Tasks with Multilinear Relationship Networks}, url = {http://arxiv.org/abs/1506.02117}, year = 2015 }