INAM: Image-Scale Neural Additive Models

Neural Additive Models are inherently interpretable models that can be applied to tabular data.However, when applying these models to images the value of a given pixel is not a meaningful feature for understanding the model.For that reason, we propose INAM -Image scale Neural Additive Model -a combination of trainable feature extractors and NAMs.We show INAMs can be successfully applied to image data sets with low variability while allowing global explanations of the models and data point-specific explanations.