  Multitask learning is a methodology to boost generalization performance and
also reduce computational intensity and memory usage. However, learning
multiple tasks simultaneously can be more difficult than learning a single task
because it can cause imbalance among tasks. To address the imbalance problem,
we propose an algorithm to balance between tasks at the gradient level by
applying gradient-based meta-learning to multitask learning. The proposed
method trains shared layers and task-specific layers separately so that the two
layers with different roles in a multitask network can be fitted to their own
purposes. In particular, the shared layer that contains informative knowledge
shared among tasks is trained by employing single gradient step update and
inner/outer loop training to mitigate the imbalance problem at the gradient
level. We apply the proposed method to various multitask computer vision
problems and achieve state-of-the-art performance.
