  In most scientific domains, the deep learning community has largely focused
on the quality of deep generative models, resulting in highly accurate and
successful solutions. However, this race for quality comes at a tremendous
computational cost, which incurs vast energy consumption and greenhouse gas
emissions. At the heart of this problem are the measures that we use as a
scientific community to evaluate our work. In this paper, we suggest relying on
a multi-objective measure based on Pareto optimality, which takes into account
both the quality of the model and its energy consumption. By applying our
measure on the current state-of-the-art in generative audio models, we show
that it can drastically change the significance of the results. We believe that
this type of metric can be widely used by the community to evaluate their work,
while putting computational cost -- and in fine energy consumption -- in the
spotlight of deep learning research.
