POEM: Pattern-Oriented Explanations of Convolutional Neural Networks

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Abstract

Convolutional Neural Networks (CNNs) are commonly used in computer vision. However, their predictions are difficult to explain, as is the case with many deep learning models. To address this problem, we present POEM, a modular framework that produces patterns of semantic concepts such as shapes and colours to explain image classifier CNNs. POEM identifies patterns such as “if sofa then living room”, meaning that if an image contains a sofa and the model pays attention to the sofa, then the model classifies the image as a living room. We illustrate the advantages of POEM over existing work using quantitative and qualitative experiments.