  Residual representation learning simplifies the optimization problem of
learning complex functions and has been widely used by traditional
convolutional neural networks. However, it has not been applied to deep neural
decision forest (NDF). In this paper we incorporate residual learning into NDF
and the resulting model achieves state-of-the-art level accuracy on three
public age estimation benchmarks while requiring less memory and computation.
We further employ gradient-based technique to visualize the decision-making
process of NDF and understand how it is influenced by facial image inputs. The
code and pre-trained models will be available at
https://github.com/Nicholasli1995/VisualizingNDF.
