  Multi-task learning (MTL) is a common paradigm that seeks to improve the
generalization performance of task learning by training related tasks
simultaneously. However, it is still a challenging problem to search the
flexible and accurate architecture that can be shared among multiple tasks. In
this paper, we propose a novel deep learning model called Task Adaptive
Activation Network (TAAN) that can automatically learn the optimal network
architecture for MTL. The main principle of TAAN is to derive flexible
activation functions for different tasks from the data with other parameters of
the network fully shared. We further propose two functional regularization
methods that improve the MTL performance of TAAN. The improved performance of
both TAAN and the regularization methods is demonstrated by comprehensive
experiments.
