  Backpropagation-based visualizations have been proposed to interpret
convolutional neural networks (CNNs), however a theory is missing to justify
their behaviors: Guided backpropagation (GBP) and deconvolutional network
(DeconvNet) generate more human-interpretable but less class-sensitive
visualizations than saliency map. Motivated by this, we develop a theoretical
explanation revealing that GBP and DeconvNet are essentially doing (partial)
image recovery which is unrelated to the network decisions. Specifically, our
analysis shows that the backward ReLU introduced by GBP and DeconvNet, and the
local connections in CNNs are the two main causes of compelling visualizations.
Extensive experiments are provided that support the theoretical analysis.
