Multi-task problem solving has been shown to improve the accuracy of the individual tasks, which is an important feature for robots, as they have a limited resource. However, when the number of labels for each task is not equal, namely imbalanced data exist, a problem may arise due to insufficient number of samples, and labeling is not very easy for mobile robots in every environment. We propose a method that can learn tasks even in the absence of the ground truth labels for some of the tasks. We also provide a detailed analysis of the proposed method. An interesting finding is related to the interaction of the tasks. We show a methodology to find out which tasks can improve the performance of other tasks. We investigate this by training the teacher network with the task outputs such as depth as inputs. We further provide empirical evidence when trained with a small amount of data. We use semantic segmentation and depth estimation tasks on different datasets, NYUDv2 and Cityscapes.

翻译：多任务问题求解已被证明能够提升各独立任务的准确性，这对于资源受限的机器人系统具有重要意义。然而，当各任务的标注数据量不均衡（即存在数据不平衡）时，样本数量不足可能导致模型性能下降，而移动机器人在不同环境中获取标注数据本身也面临困难。本文提出一种能够在部分任务缺失真实标注的情况下进行多任务学习的方法，并对所提方法进行了详细分析。一个有趣的发现涉及任务间的相互作用机制：我们提出一种方法论来识别哪些任务能够提升其他任务的性能，具体通过将深度估计等任务输出作为教师网络的输入进行训练来实现这一分析。此外，我们提供了在小规模数据训练场景下的实证依据。实验在NYUDv2和Cityscapes数据集上，分别针对语义分割与深度估计任务展开验证。