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Multi-task learning promises better model generalization on a target task byjointly optimizing it with an auxiliary task. However, the current practicerequires additional labeling efforts for the auxiliary task, while notguaranteeing better model performance. In this paper, we find that jointlytraining a dense prediction (target) task with a self-supervised (auxiliary)task can consistently improve the performance of the target task, whileeliminating the need for labeling auxiliary tasks. We refer to this jointtraining as Composite Learning (CompL). Experiments of CompL on monocular depthestimation, semantic segmentation, and boundary detection show consistentperformance improvements in fully and partially labeled datasets. Furtheranalysis on depth estimation reveals that joint training with self-supervisionoutperforms most labeled auxiliary tasks. We also find that CompL can improvemodel robustness when the models are evaluated in new domains. These resultsdemonstrate the benefits of self-supervision as an auxiliary task, andestablish the design of novel task-specific self-supervised methods as a newaxis of investigation for future multi-task learning research.

Composite Learning for Robust and Effective Dense Predictions