Gradient Surgery for Multi-Task Learning

• URL: http://arxiv.org/abs/2001.06782v4
• Date: Tue, 22 Dec 2020 00:35:46 GMT
• Title: Gradient Surgery for Multi-Task Learning
• Authors: Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
• Abstract summary: Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks. The reasons why multi-task learning is so challenging compared to single-task learning are not fully understood. We propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of any other task that has a conflicting gradient.
• Score: 119.675492088251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge. Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks to enable more efficient learning. However, the multi-task setting presents a number of optimization challenges, making it difficult to realize large efficiency gains compared to learning tasks independently. The reasons why multi-task learning is so challenging compared to single-task learning are not fully understood. In this work, we identify a set of three conditions of the multi-task optimization landscape that cause detrimental gradient interference, and develop a simple yet general approach for avoiding such interference between task gradients. We propose a form of gradient surgery that projects a task's gradient onto the normal plane of the gradient of any other task that has a conflicting gradient. On a series of challenging multi-task supervised and multi-task RL problems, this approach leads to substantial gains in efficiency and performance. Further, it is model-agnostic and can be combined with previously-proposed multi-task architectures for enhanced performance.

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