  Partial multi-task learning where training examples are annotated for one of
the target tasks is a promising idea in remote sensing as it allows combining
datasets annotated for different tasks and predicting more tasks with fewer
network parameters. The na\"ive approach to partial multi-task learning is
sub-optimal due to the lack of all-task annotations for learning joint
representations. This paper proposes using knowledge distillation to replace
the need of ground truths for the alternate task and enhance the performance of
such approach. Experiments conducted on the public ISPRS 2D Semantic Labeling
Contest dataset show the effectiveness of the proposed idea on partial
multi-task learning for semantic tasks including object detection and semantic
segmentation in aerial images.
