Dilated convolution.

<div><p>This study aims to explore a data-driven cultural background fusion method to improve the accuracy of environmental art image classification. A novel Dual Kernel Squeeze and Excitation Network (DKSE-Net) model is proposed for the complex cultural background and diverse visual rep...

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Bibliographic Details
Main Author:	Chenchen Liu (503953) (author)
Other Authors:	Haoyue Guo (3991031) (author)
Published:	2025
Subjects:	Biotechnology Ecology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified receptive fields using experimental results indicate diverse visual representation deep learning technology 7 %, 3 complex cultural background selective kernel network model &# 8217 complex cultural backgrounds global average pooling environmental art images dual kernel squeeze feature capture ability layer convolution process second layer convolution higher classification accuracy net model achieves cultural backgrounds initial layer processing images generalization ability excitation network classification accuracy art models feature maps feature extraction model combines pointwise convolution dilated convolution depthwise convolution xlink "> thus enhancing technical support study aims senet ). prevent overfitting performance provides local features l2 regularization important reference existing state comprehensively extract comparative models broad potential batch normalization adaptive adjustment
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Dilated convolution.

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