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Multitask learning is a framework that enforces multiple learning tasks toshare knowledge to improve their generalization abilities. While shallowmultitask learning can learn task relations, it can only handle predefinedfeatures. Modern deep multitask learning can jointly learn latent features andtask sharing, but they are obscure in task relation. Also, they predefine whichlayers and neurons should share across tasks and cannot learn adaptively. Toaddress these challenges, this paper proposes a new multitask learningframework that jointly learns latent features and explicit task relations bycomplementing the strength of existing shallow and deep multitask learningscenarios. Specifically, we propose to model the task relation as thesimilarity between task input gradients, with a theoretical analysis of theirequivalency. In addition, we innovatively propose a multitask learningobjective that explicitly learns task relations by a new regularizer.Theoretical analysis shows that the generalizability error has been reducedthanks to the proposed regularizer. Extensive experiments on several multitasklearning and image classification benchmarks demonstrate the proposed methodeffectiveness, efficiency as well as reasonableness in the learned taskrelation patterns.

Saliency-Regularized Deep Multi-Task Learning