  Multi-Task Learning is a learning paradigm that uses correlated tasks to
improve performance generalization. A common way to learn multiple tasks is
through the hard parameter sharing approach, in which a single architecture is
used to share the same subset of parameters, creating an inductive bias between
them during the training process. Due to its simplicity, potential to improve
generalization, and reduce computational cost, it has gained the attention of
the scientific and industrial communities. However, tasks often conflict with
each other, which makes it challenging to define how the gradients of multiple
tasks should be combined to allow simultaneous learning. To address this
problem, we use the idea of multi-objective optimization to propose a method
that takes into account temporal behaviour of the gradients to create a dynamic
bias that adjust the importance of each task during the backpropagation. The
result of this method is to give more attention to the tasks that are diverging
or that are not being benefited during the last iterations, allowing to ensure
that the simultaneous learning is heading to the performance maximization of
all tasks. As a result, we empirically show that the proposed method
outperforms the state-of-art approaches on learning conflicting tasks. Unlike
the adopted baselines, our method ensures that all tasks reach good
generalization performances.
