A light-weight method to foster the (Grad)CAM interpretability and explainability of classification networks
This is an incremental improvement for embedded systems and standard deep architectures, addressing the need for more explainable AI in resource-constrained environments.
The paper tackles the problem of improving the interpretability of classification networks using (Grad)CAM maps by modifying the training loss without adding structural elements, resulting in enhanced interpretability as measured by several indicators.
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.