AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

Abstract
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, called AdaShare, that decides what...
Paper Details
Title
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Published Date
Nov 27, 2019
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