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 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:
this https URL

本文提出了一种自适应共享方法 AdaShare，通过任务特定的策略选择性地决定跨哪些任务共享众多的深度神经网络层，从而实现最佳识别准确度和资源效率。实验表明，该方法针对具有多个任务的不同基准数据集具有优异的效果。