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Multi-task learning (MTL) can improve the generalization performance ofneural networks by sharing representations with related tasks. Nonetheless, MTLcan also degrade performance through harmful interference between tasks. Recentwork has pursued task-specific loss weighting as a solution for thisinterference. However, existing algorithms treat tasks as atomic, lacking theability to explicitly separate harmful and helpful signals beyond the tasklevel. To this end, we propose SLGrad, a sample-level weighting algorithm formulti-task learning with auxiliary tasks. Through sample-specific task weights,SLGrad reshapes the task distributions during training to eliminate harmfulauxiliary signals and augment useful task signals. Substantial generalizationperformance gains are observed on (semi-) synthetic datasets and commonsupervised multi-task problems.

Sample-Level Weighting for Multi-Task Learning with Auxiliary Tasks