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There are many ways of interpreting the world and they are highlyinterdependent. We exploit such complex dependencies and introduce a powerfulmulti-task hypergraph, in which every node is a task and different pathsthrough the hypergraph reaching a given task become unsupervised teachers, byforming 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 astudent of the self-supervised hypergraph system. We apply our model to one ofthe most important problems of our times, that of Earth Observation, which ishighly multi-task and it often suffers from missing ground-truth data. Byperforming extensive experiments on the NASA NEO Dataset, spanning a period of22 years, we demonstrate the value of our multi-task semi-supervised approach,by consistent improvements over strong baselines and recent work. We also showthat the hypergraph can adapt unsupervised to gradual data distribution shiftsand reliably recover, through its multi-task self-supervision process, themissing data for several observational layers for up to seven years.

Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations