  We propose an information-fusion approach based on belief functions to
combine convolutional neural networks. In this approach, several pre-trained
DS-based CNN architectures extract features from input images and convert them
into mass functions on different frames of discernment. A fusion module then
aggregates these mass functions using Dempster's rule. An end-to-end learning
procedure allows us to fine-tune the overall architecture using a learning set
with soft labels, which further improves the classification performance. The
effectiveness of this approach is demonstrated experimentally using three
benchmark databases.
