  In real-world scenarios we often need to perform multiple tasks
simultaneously. Multi-Task Learning (MTL) is an adequate method to do so, but
usually requires datasets labeled for all tasks. We propose a method that can
leverage datasets labeled for only some of the tasks in the MTL framework. Our
work, Knowledge Assembly (KA), learns multiple tasks from disjoint datasets by
leveraging the unlabeled data in a semi-supervised manner, using model
augmentation for pseudo-supervision. Whilst KA can be implemented on any
existing MTL networks, we test our method on jointly learning person
re-identification (reID) and pedestrian attribute recognition (PAR). We surpass
the single task fully-supervised performance by $4.2\%$ points for reID and
$0.9\%$ points for PAR.
