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Recent works have shown that deep neural networks benefit from multi-tasklearning by learning a shared representation across several related tasks.However, performance of such systems depend on relative weighting betweenvarious losses involved during training. Prior works on loss weighting schemesassume that instances are equally easy or hard for all tasks. In order to breakthis assumption, we let the training process dictate the optimal weighting oftasks for every instance in the dataset. More specifically, we equip everyinstance in the dataset with a set of learnable parameters (instance-level taskparameters) where the cardinality is equal to the number of tasks learned bythe model. These parameters model the weighting of each task for an instance.They are updated by gradient descent and do not require hand-crafted rules. Weconduct extensive experiments on SURREAL and CityScapes datasets, for humanshape and pose estimation, depth estimation and semantic segmentation tasks. Inthese tasks, our approach outperforms recent dynamic loss weighting approaches,e.g. reducing surface estimation errors by 8.97% on SURREAL. When applied todatasets where one or more tasks can have noisy annotations, the proposedmethod learns to prioritize learning from clean labels for a given task, e.g.reducing surface estimation errors by up to 60%. We also show that we canreliably detect corrupt labels for a given task as a by-product from learnedinstance-level task parameters.

Instance-Level Task Parameters: A Robust Multi-task Weighting Framework