Improving the Interpretability of GradCAMs in Deep Classification Networks
Alfred Schöttl
Abstract
Deep classification networks play an important role as backbone networks in industrial AI applications. These applications are often cost or safety critical; explainability of the AI results is a highly demanded feature. We introduce CAM fostering, a method to improve the explainability of classification nets based on local layers such as convolutional or pooling layers. Several CAM interpretability measures are defined and used as additional loss terms. Even though the method requires second-order derivatives, it is demonstrated that deep nets can be trained on large datasets without frozen parameters. The training parameters can be chosen such that the accuracy degradation remains decent in favor of the CAM interpretability improvement. We conclude by comparing the results of different training parameter configurations.