A novel Fourier neural operator framework for classification of multi-sized images: Application to 3D digital porous media

arXiv:2402.11568v1 Announce Type: new
Abstract: Fourier neural operators (FNOs) are invariant with respect to the size of input images, and thus images with any size can be fed into FNO-based frameworks without any modification of network architectures, in contrast to traditional convolutional neural networks (CNNs). Leveraging the advantage of FNOs, we propose a novel deep-learning framework for classifying images with varying sizes. Particularly, we simultaneously train the proposed network on multi-sized images. As a practical application, we consider the problem …

abstract application architectures arxiv classification contrast cs.cv digital fed fourier framework frameworks images media network novel operators type