Why do CNNs excel at feature extraction? A mathematical explanation

• URL: http://arxiv.org/abs/2307.00919v1
• Date: Mon, 3 Jul 2023 10:41:34 GMT
• Title: Why do CNNs excel at feature extraction? A mathematical explanation
• Authors: Vinoth Nandakumar, Arush Tagade, Tongliang Liu
• Abstract summary: We introduce a novel model for image classification, based on feature extraction, that can be used to generate images resembling real-world datasets. In our proof, we construct piecewise linear functions that detect the presence of features, and show that they can be realized by a convolutional network.
• Score: 53.807657273043446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Over the past decade deep learning has revolutionized the field of computer vision, with convolutional neural network models proving to be very effective for image classification benchmarks. However, a fundamental theoretical questions remain answered: why can they solve discrete image classification tasks that involve feature extraction? We address this question in this paper by introducing a novel mathematical model for image classification, based on feature extraction, that can be used to generate images resembling real-world datasets. We show that convolutional neural network classifiers can solve these image classification tasks with zero error. In our proof, we construct piecewise linear functions that detect the presence of features, and show that they can be realized by a convolutional network.

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