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Received: 19 June 2023Revised: 24 September 2023IET Image ProcessingAccepted: 24 October 2023DOI: 10.1049/ipr2.12971ORIGINAL RESEARCHA DenseNetbased feature weighting convolutional network recognition
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A densenetbased feature weighting is a method used in machine learning and image processing to assign importance to various features extracted by a DenseNet architecture, allowing for a more refined understanding of the data based on which features contribute most to performance.
Typically, individuals or organizations that utilize a densenetbased approach in their machine learning models may be required to file a densenetbased feature weighting, particularly in compliance with regulatory or research standards.
To fill out a densenetbased feature weighting, one should specify the features derived from the DenseNet model, assign numerical values representing their importance, and ensure that the document adheres to any required formats or guidelines set by the regulatory body.
The purpose of a densenetbased feature weighting is to enhance model interpretability and performance by identifying which features are most impactful in making predictions, thus guiding improvements or adjustments in the model.
Information that must be reported includes the list of features, the corresponding weight or importance assigned to each feature, the methodology used to determine these weights, and possibly performance metrics related to the feature weighting.
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