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AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Ximeng Sun · Rameswar Panda · Rogerio Feris · Kate Saenko

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1115

Multi-task learning is an open and challenging problem in computer vision. The typical way of conducting multi-task learning with deep neural networks is either through handcrafted schemes that share all initial layers and branch out at an adhoc point, or through separate task-specific networks with an additional feature sharing/fusion mechanism. Unlike existing methods, we propose an adaptive sharing approach, calledAdaShare, that decides what to share across which tasks to achieve the best recognition accuracy, while taking resource efficiency into account. Specifically, our main idea is to learn the sharing pattern through a task-specific policy that selectively chooses which layers to execute for a given task in the multi-task network. We efficiently optimize the task-specific policy jointly with the network weights, using standard back-propagation. Experiments on several challenging and diverse benchmark datasets with a variable number of tasks well demonstrate the efficacy of our approach over state-of-the-art methods. Project page: https://cs-people.bu.edu/sunxm/AdaShare/project.html

Author Information

Ximeng Sun (Boston University)
Rameswar Panda (MIT-IBM Watson AI Lab)
Rogerio Feris (MIT-IBM Watson AI Lab, IBM Research)
Kate Saenko (Boston University & MIT-IBM Watson AI Lab, IBM Research)

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