In this paper, we present a new MTL framework that searches for structures
optimized for multiple tasks with diverse graph topologies and shares features
among tasks. We design a restricted DAG-based central network with
read-in/read-out layers to build topologically diverse task-adaptive structures
while limiting search space and time. We search for a single optimized network
that serves as multiple task adaptive sub-networks using our three-stage
training process. To make the network compact and discretized, we propose a
flow-based reduction algorithm and a squeeze loss used in the training process.
We evaluate our optimized network on various public MTL datasets and show ours
achieves state-of-the-art performance. An extensive ablation study
experimentally validates the effectiveness of the sub-module and schemes in our
framework.

本文提出一个多任务学习框架，利用限制 DAG-based 中央网络和特征共享来构建适应性强的结构，并使用流量约束和挤压损失将网络压缩离散化，从而实现在多个公共 MTL 数据集上达到最先进的性能。