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• W4385484725 abstract "Style transfer is an application that has increased interest, primarily because of the impressive results obtained using neural networks. However, this application demands a lot of computational resources, thus preventing its use in low-end mobiles. The patch-based approach is an interesting alternative that consumes less memory. This work uses two methods to generate high-resolution stylized images. Gated convolution in the neural network and the Halide language in the patch-based implementation are used for optimization. As a result, we were able to apply the style transfer method on a mobile device with 4 GB of memory RAM running under 6 seconds for <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$1920times 1080$</tex> image size preserving the high-frequency details." @default.
• W4385484725 created "2023-08-03" @default.
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• W4385484725 date "2023-06-18" @default.
• W4385484725 modified "2023-09-26" @default.
• W4385484725 title "A Neurally Guided Patch-Based Style Transfer for Mobile Devices" @default.
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• W4385484725 doi "https://doi.org/10.1109/ijcnn54540.2023.10191801" @default.
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