Many machine learning applications, for example, perception for autonomous driving, require solving multiple tasks simultaneously. This however, can prove challenging during training as the different objectives can interfere, which can result in sub-par results. We are interested in how multiple conflicting objectives can be best combined to reach a model that performs well on all tasks.