There are many ways of interpreting the world and they are highly
interdependent. We exploit such complex dependencies and introduce a powerful
multi-task hypergraph, in which every node is a task and different paths
through the hypergraph reaching a given task become unsupervised teachers, by
forming ensembles that learn to generate reliable pseudolabels for that task.
Each hyperedge is part of an ensemble teacher for a given task and it is also a
student of the self-supervised hypergraph system. We apply our model to one of
the most important problems of our times, that of Earth Observation, which is
highly multi-task and it often suffers from missing ground-truth data. By
performing extensive experiments on the NASA NEO Dataset, spanning a period of
22 years, we demonstrate the value of our multi-task semi-supervised approach,
by consistent improvements over strong baselines and recent work. We also show
that the hypergraph can adapt unsupervised to gradual data distribution shifts
and reliably recover, through its multi-task self-supervision process, the
missing data for several observational layers for up to seven years.

通过引入强大的多任务超图模型，利用复杂的依赖关系，构建每个节点作为一个任务、超图中到达给定任务的不同路径成为无监督教师的多任务超图，并能够适应渐变的数据分布转变，可靠地补充缺失的地球观测数据，从而实现对多任务的半监督学习的改进。