  Are deep convolutional neural networks (CNNs) for image classification
explainable by utility maximization with information acquisition costs? We
demonstrate that deep CNNs behave equivalently (in terms of necessary and
sufficient conditions) to rationally inattentive utility maximizers, a
generative model used extensively in economics for human decision making. Our
claim is based by extensive experiments on 200 deep CNNs from 5 popular
architectures. The parameters of our interpretable model are computed
efficiently via convex feasibility algorithms. As an application, we show that
our economics-based interpretable model can predict the classification
performance of deep CNNs trained with arbitrary parameters with accuracy
exceeding 94% . This eliminates the need to re-train the deep CNNs for image
classification. The theoretical foundation of our approach lies in Bayesian
revealed preference studied in micro-economics. All our results are on GitHub
and completely reproducible.
