Robust Multitask Learning With Sample Gradient Similarity
提出样本梯度相似性概念，衡量任务样本梯度与真实梯度的一致性，据此为更一致的任务和更鲁棒的训练样本分配更大权重，提升多任务学习性能。
Multitask learning has led to great success in many deep learning applications during the last decade. However, recent experiments have demonstrated that the performance of multitask learning depends on how to balance the relationship between different tasks. Therefore, many approaches have been proposed to adjust per-task gradient directions or design a more appropriate task reweighting scheme based on task-level statistics. In this article, we discuss how to boost the performance of multitask learning by using more fine-grained sample gradient information. To this end, we propose the concept of sample gradient similarity, which measures the agreement between the sample gradient for a task and the true gradient. Based on this concept, greater weight is assigned to more consistent tasks and more robust training samples to improve the training process of multitask learning. Extensive experimental results show that our proposed method outperforms the state-of-the-art algorithms on a series of challenging multitask datasets.