  Saliency methods generating visual explanatory maps representing the
importance of image pixels for model classification is a popular technique for
explaining neural network decisions. Hierarchical dynamic masks (HDM), a novel
explanatory maps generation method, is proposed in this paper to enhance the
granularity and comprehensiveness of saliency maps. First, we suggest the
dynamic masks (DM), which enables multiple small-sized benchmark mask vectors
to roughly learn the critical information in the image through an optimization
method. Then the benchmark mask vectors guide the learning of large-sized
auxiliary mask vectors so that their superimposed mask can accurately learn
fine-grained pixel importance information and reduce the sensitivity to
adversarial perturbations. In addition, we construct the HDM by concatenating
DM modules. These DM modules are used to find and fuse the regions of interest
in the remaining neural network classification decisions in the mask image in a
learning-based way. Since HDM forces DM to perform importance analysis in
different areas, it makes the fused saliency map more comprehensive. The
proposed method outperformed previous approaches significantly in terms of
recognition and localization capabilities when tested on natural and medical
datasets.
