We propose a novel multi-task learning architecture, which allows learning of
task-specific feature-level attention. Our design, the Multi-Task Attention
Network (MTAN), consists of a single shared network containing a global feature
pool, together with a soft-attention module for each task. These modules allow
for learning of task-specific features from the global features, whilst
simultaneously allowing for features to be shared across different tasks. The
architecture can be trained end-to-end and can be built upon any feed-forward
neural network, is simple to implement, and is parameter efficient. We evaluate
our approach on a variety of datasets, across both image-to-image predictions
and image classification tasks. We show that our architecture is
state-of-the-art in multi-task learning compared to existing methods, and is
also less sensitive to various weighting schemes in the multi-task loss
function. Code is available at this https URL

本文提出了一种新的多任务学习架构，其中包含了任务特定的特征级别的注意力学习，称之为 Multi-Task Attention Network (MTAN)，它可以在任何前馈神经网络的基础上进行端到端训练，本文在图像分类任务和图像到图像预测上进行了验证，发现相对于现有方法，该架构达到了最先进的水平，而且对多任务损失函数的各种加权方案也不敏感。