However, as depicted in Figure 2(b), in MAT-INF, our jointly labeled fashion can guarantee that X remains the same as in a single task and only Y is added.
mATINF provides a fair and ideal stage for exploring multi-task learning, especially auxiliary and multi-task supervision under a single dataset.