<p>While deep learning systems demonstrate good performance in many fields such as computer vision, natural language processing, and computational biology, time and data efficiency still remain as two major challenges. Deep multi-task learning, in which one network produces predictive outputs for multiple tasks, has emerged as a promising approach with fast inference and good performance. However, how to balance the learning of each individual task is difficult in deep multi-task learning. In this paper, we present a combinational method called POMSI to project conflicting gradients and mitigate the scale imbalance in multi-task learning. The proposed POMSI method can be trained end-to-end with all kinds of losses without any distributional assumption. Moreover, the POMSI model is model-agnostic and can be applied to existing multi-task architectures for further enhancement. Through extensive experiments on benchmark datasets, the proposed POMSI method achieves substantial gains in the performance compared with state-of-the-art methods.</p>