  Convolutional neural networks (CNNs) have achieved astonishing performance on
various image classification tasks, but it is difficult for humans to
understand how a classification comes about. Recent literature proposes methods
to explain the classification process to humans. These focus mostly on
visualizing feature maps and filter weights, which are not very intuitive for
non-experts in analyzing a CNN classification. In this paper, we propose
FilTag, an approach to effectively explain CNNs even to non-experts. The idea
is that when images of a class frequently activate a convolutional filter, then
that filter is tagged with that class. These tags provide an explanation to a
reference of a class-specific feature detected by the filter. Based on the
tagging, individual image classifications can then be intuitively explained in
terms of the tags of the filters that the input image activates. Finally, we
show that the tags are helpful in analyzing classification errors caused by
noisy input images and that the tags can be further processed by machines.
