2017introduced sluice networks as a generalization and combination of various MTL architectures that use different information transfer techniques.
it can emulate, e.g., hard parameter sharing (Caruana, 1993), where the same layer weights (i.e., the same layers) are used for different tasks, and cross-stitch networks (Alonso and Plank, 2017), in which trainable parameters decide the amount of shared information between layers of separate networks.