  It is widely recognized that the predictions of deep neural networks are
difficult to parse relative to simpler approaches. However, the development of
methods to investigate the mode of operation of such models has advanced
rapidly in the past few years. Recent work introduced an intuitive framework
which utilizes generative models to improve on the meaningfulness of such
explanations. In this work, we display the flexibility of this method to
interpret diverse and challenging modalities: music and physical simulations of
urban environments.
