Third, in addition to handcrafted loss functions, we further ask whether the joint MRT model could benefit from automatic "loss engineering".
let Γ(ŷ) be the loss learned from the training set, we augment ∆(ŷ, y) of the MRT objective with Γ(ŷ), and require the learning process to assign a smaller Γ value (with a margin) to the ground truth output y than otherŷ ∈ Y \{y}, min .