Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models for tasks like image classification or visual question answering.   In this paper, we introduce an explanation approach for image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification.   In this task, an explanation depends on both of the input images, so standard methods do not apply. We propose an explanation method that pairs a saliency map identifying important image regions with an attribute that best explains the match.   We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition. Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2.
A black box approach for explaining the predictions of an image similarity model. Introduces method for image similarity model explanation which identifies attributes that contribute positively to the similarity score and pairs them with a generated saliency map. The paper proposes an explanation mechanism that pairs the typical saliency map regions together with attributes for similarity matching deep neural networks.