Right for the Right Reason: Making Image Classification Robust

Convolutional neural networks (CNNs) have achieved astonishing performance on various image classification tasks. Although such models classify most images correctly, they do not provide any explanation for their decisions. Recently, there have been attempts to provide such an explanation by determining which parts of the input image the classifier focuses on most. It turns out that many models output the correct classification, but for the wrong reason (e.g., based on irrelevant parts of the image). In this paper, we propose a new score for automatically quantifying to which degree the model focuses on the right image parts. The score is calculated by considering the degree to which the most decisive image regions - given by applying an explainer to the CNN model - overlap with the silhouette of the object to be classified. In extensive experiments using VGG16, ResNet, and MobileNet as CNNs, Occlusion, LIME, and Grad-Cam/Grad-Cam++ as explanation methods, and Dogs vs. Cats and Caltech 101 as data sets, we can show that our metric can indeed be used for making CNN models for image classification more robust while keeping their accuracy.