用于鲁棒且有效的稠密预测的复合学习
Composite Learning for Robust and Effective Dense Predictions
摘要：
多任务学习通过与辅助任务联合优化目标任务，保证了模型在目标任务上的更好泛化。然而，目前的做法需要额外的标记工作的辅助任务，而不能保证更好的模型性能。在本文中，我们发现，联合训练密集预测（目标）任务和自监督（辅助）任务可以持续提高目标任务的性能，同时消除标记辅助任务的需要。我们将这种联合训练称为复合学习（CompL）。CompL在单目深度估计、语义分割和边界检测上的实验表明，在完全标记和部分标记的数据集中，CompL的性能得到了一致的提高。对深度估计的进一步分析表明，具有自我监督的联合训练优于大多数标记辅助任务。我们还发现，CompL可以提高模型的鲁棒性时，模型在新的领域进行评估。这些结果证明了自我监督作为一种辅助任务的好处，并建立了新的任务特定的自我监督方法的设计，作为未来多任务学习研究的一个新的调查轴。
英文摘要：
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 a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks. We refer to this joint training as Composite Learning (CompL). Experiments of CompL on monocular depth estimation, semantic segmentation, and boundary detection show consistent performance improvements in fully and partially labeled datasets. Further analysis on depth estimation reveals that joint training with self-supervision outperforms most labeled auxiliary tasks. We also find that CompL can improve model robustness when the models are evaluated in new domains. These results demonstrate the benefits of self-supervision as an auxiliary task, and establish the design of novel task-specific self-supervised methods as a new axis of investigation for future multi-task learning research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
