  Deep neural decision forest (NDF) achieved remarkable performance on various
vision tasks via combining decision tree and deep representation learning. In
this work, we first trace the decision-making process of this model and
visualize saliency maps to understand which portion of the input influence it
more for both classification and regression problems. We then apply NDF on a
multi-task coordinate regression problem and demonstrate the distribution of
routing probabilities, which is vital for interpreting NDF yet not shown for
regression problems. The pre-trained model and code for visualization will be
available at https://github.com/Nicholasli1995/VisualizingNDF
