2023/07/03 by Vinoth Nandakumar, Arush Tagade, Nandakumar, Vinoth +3 · 1 citation
Computer Science · #Machine Learning and Data Classification #Medical Image Segmentation Techniques #Neural Networks and Applications #cs.AI #cs.CV
paper · pdf · doi:10.48550/arxiv.2307.00919
openalex publication_date 2023/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.