  We consider a light-weight method which allows to improve the explainability
of localized classification networks. The method considers (Grad)CAM maps
during the training process by modification of the training loss and does not
require additional structural elements. It is demonstrated that the (Grad)CAM
interpretability, as measured by several indicators, can be improved in this
way. Since the method shall be applicable on embedded systems and on standard
deeper architectures, it essentially takes advantage of second order
derivatives during the training and does not require additional model layers.
