A light-weight method to foster the (Grad)CAM interpretability and explainability of classification networks

TL;DR

This paper introduces a lightweight training method that enhances the interpretability of classification networks using (Grad)CAM maps without adding extra model components, suitable for embedded systems.

Contribution

The proposed method improves (Grad)CAM interpretability by modifying the training loss, requiring no additional structural elements or model layers.

Findings

Enhanced interpretability as measured by multiple indicators.

Applicable to embedded systems and standard architectures.

Utilizes second order derivatives during training.

Abstract

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.