Human-Understandable Decision Making for Visual Recognition
  The widespread use of deep neural networks has achieved substantial success
in many tasks. However, there still exists a huge gap between the operating
mechanism of deep learning models and human-understandable decision making, so
that humans cannot fully trust the predictions made by these models. To date,
little work has been done on how to align the behaviors of deep learning models
with human perception in order to train a human-understandable model. To fill
this gap, we propose a new framework to train a deep neural network by
incorporating the prior of human perception into the model learning process.
Our proposed model mimics the process of perceiving conceptual parts from
images and assessing their relative contributions towards the final
recognition. The effectiveness of our proposed model is evaluated on two
classical visual recognition tasks. The experimental results and analysis
confirm our model is able to provide interpretable explanations for its
predictions, but also maintain competitive recognition accuracy.
