First, for MTL, we propose Task-Adaptive Representation learning using Soft-coding, namely TARS, wherein shared and private features are both mixtures of features.
as shown in Figure 1c, TARS begins as a generic sharing framework using one common shared encoder, but also adopts its paired task-specific layers to feed a Mixture-of-Experts (MoE) module (Shazeer et al., 2017;Guo et al., 2018) which captures soft-private features with a weighted combination of all task-dependent features, where a gating network G in Figure 1c, decides on output weights for each task.