  The black-box nature of Deep Neural Networks (DNNs) severely hinders its
performance improvement and application in specific scenes. In recent years,
class activation mapping-based method has been widely used to interpret the
internal decisions of models in computer vision tasks. However, when this
method uses backpropagation to obtain gradients, it will cause noise in the
saliency map, and even locate features that are irrelevant to decisions. In
this paper, we propose an Absolute value Class Activation Mapping-based
(Abs-CAM) method, which optimizes the gradients derived from the
backpropagation and turns all of them into positive gradients to enhance the
visual features of output neurons' activation, and improve the localization
ability of the saliency map. The framework of Abs-CAM is divided into two
phases: generating initial saliency map and generating final saliency map. The
first phase improves the localization ability of the saliency map by optimizing
the gradient, and the second phase linearly combines the initial saliency map
with the original image to enhance the semantic information of the saliency
map. We conduct qualitative and quantitative evaluation of the proposed method,
including Deletion, Insertion, and Pointing Game. The experimental results show
that the Abs-CAM can obviously eliminate the noise in the saliency map, and can
better locate the features related to decisions, and is superior to the
previous methods in recognition and localization tasks.
