Multi-task Learning (MTL) refers to machine learning methods that build models by leveraging many tasks either sequentially or concurrently [ 1, 2, 10 ]. When tasks are addressed sequentially, the MTL approach is to build a model using the first task and later to refine the model using the second task.. In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of per-task losses. However, this workaround is only valid when the tasks do.

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Multi-Task Learning as Multi-Objective Optimization. Ozan Sener, Vladlen Koltun. In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy.. Multi-Task Learning can be viewed as a form of Multi-Objective Optimization in which the goal is to simultaneously optimize several main tasks [].In this approach, the DNN is trained to jointly optimize the loss functions of all the tasks, while learning shared representations that capture the common features across the tasks, and task-specific representations that capture the unique features.