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HCFNN: High-order coverage function neural network for image classification
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DOI：10.1016/j.patcog.2022.108873.png)
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Abstract
En 中文
Recent advances in deep neural networks (DNNs) have mainly focused on innovations in network ar-chitecture and loss function. In this paper, we introduce a flexible high-order coverage function (HCF) neuron model to replace the fully-connected (FC) layers. The approximation theorem and proof for the HCF are also presented to demonstrate its fitting ability. Unlike the FC layers, which cannot handle high-dimensional data well, the HCF utilizes weight coefficients and hyper-parameters to mine under-lying geometries with arbitrary shapes in an n-dimensional space. To explore the power and poten-tial of our HCF neuron model, a high-order coverage function neural network (HCFNN) is proposed, which incorporates the HCF neuron as the building block. Moreover, a novel adaptive optimization method for weights and hyper-parameters is designed to achieve effective network learning. Compre-hensive experiments on nine datasets in several domains validate the effectiveness and generalizability of the HCF and HCFNN. The proposed method provides a new perspective for further developments in DNNs and ensures wide application in the field of image classification. The source code is available at https://github.com/Tough2011/HCFNet.git (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
DNNs
Neuron modeling
Heuristic algorithm
Back propagation
Computer vision
Journal
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