In this work we address task interference in universal networks by
considering that a network is trained on multiple tasks, but performs one task
at a time, an approach we refer to as "single-tasking multiple tasks". The
network thus modifies its behaviour through task-dependent feature adaptation,
or task attention. This gives the network the ability to accentuate the
features that are adapted to a task, while shunning irrelevant ones. We further
reduce task interference by forcing the task gradients to be statistically
indistinguishable through adversarial training, ensuring that the common
backbone architecture serving all tasks is not dominated by any of the
task-specific gradients. Results in three multi-task dense labelling problems
consistently show: (i) a large reduction in the number of parameters while
preserving, or even improving performance and (ii) a smooth trade-off between
computation and multi-task accuracy. We provide our system's code and
pre-trained models at this http URL

通过单任务多任务方法，使用任务依赖的特征适应或任务关注来减少神经网络中的任务干扰，并通过敌对训练使任务梯度具有相同的统计学特性，从而消除任务特定梯度对神经网络的影响，实现了在多任务场景下参数规模和性能的平衡。