Composite Learning for Robust and Effective Dense Predictions
Composite Learning for Robust and Effective Dense Predictions
Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. However, the current practice requires additional labeling efforts for the auxiliary task, while not guaranteeing better model performance. In this paper, we find that jointly training a dense prediction (target) task with …