This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition

By Meike Nauta, Annemarie Jutte, Jesper C. Provoost, Christin Seifert. 2020

75 citations recorded by the source corpus.

Image recognition with prototypes is considered an interpretable alternative for black box deep learning models. Classification depends on the extent to which a test image "looks like" a prototype. However, perceptual similarity for humans can be different from the similarity learnt by the model. A user is unaware of the underlying classification strategy and does not know which image characteristics (e.g., color or shape) is the dominant characteristic for the decision. We address this ambiguity and argue that prototypes should be explained. Only visualizing prototypes can be insufficient for understanding what a prototype exactly represents, and why a prototype and an image are considered similar. We improve interpretability by automatically enhancing prototypes with extra information about visual characteristics considered important by the model. Specifically, our method quantifies the influence of color hue, shape, texture, contrast and saturation in a prototype. We apply our method to the existing Prototypical Part Network (ProtoPNet) and show that our explanations clarify the meaning of a prototype which might have been interpreted incorrectly otherwise. We also reveal that visually similar prototypes can have the same explanations, indicating redundancy. Because of the generality of our approach, it can improve the interpretability of any similarity-based method for prototypical image recognition.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “In the image recognition domain, Nauta et al. (2020a) proposed a method for explaining what visual characteristics a prototype (in a trained ProtoPNet) is looking for; Nauta et al. (2020b) proposed a method for learning neural prototype trees based on a prototype layer; Rymarczyk et al. (2020)…”
Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2020, background): “The visualized prototypes could therefore be further explained by indicating whether e.g . color or shape was most important, as presented by [37].”