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• W3191738981 endingPage "7195" @default.
• W3191738981 startingPage "7195" @default.
• W3191738981 abstract "Ordered Weighted Averaging (OWA) operators have been integrated in Convolutional Neural Networks (CNNs) for image classification through the OWA layer. This layer lets the CNN integrate global information about the image in the early stages, where most CNN architectures only allow for the exploitation of local information. As a side effect of this integration, the OWA layer becomes a practical method for the determination of OWA operator weights, which is usually a difficult task that complicates the integration of these operators in other fields. In this paper, we explore the weights learned for the OWA operators inside the OWA layer, characterizing them through their basic properties of orness and dispersion. We also compare them to some families of OWA operators, namely the Binomial OWA operator, the Stancu OWA operator and the exponential RIM OWA operator, finding examples that are currently impossible to generalize through these parameterizations." @default.
• W3191738981 created "2021-08-16" @default.
• W3191738981 creator A5027737844 @default.
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• W3191738981 date "2021-08-04" @default.
• W3191738981 modified "2023-10-14" @default.
• W3191738981 title "A Study of OWA Operators Learned in Convolutional Neural Networks" @default.
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• W3191738981 doi "https://doi.org/10.3390/app11167195" @default.
• W3191738981 hasPublicationYear "2021" @default.
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