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- W4312014315 abstract "Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such test-time training for style transfer, we present novel network architectures, with two sub-modules of correspondence and generation modules, and loss functions consisting of contrastive content, style, and cycle consistency losses. Our framework does not require offline training phase for style transfer, which has been one of the main challenges in existing methods, but the networks are to be solely learned during test-time. Experimental results prove that our framework has a better generalization ability to unseen image pairs and even outperforms the state-of-the-art methods." @default.
- W4312014315 created "2023-01-03" @default.
- W4312014315 creator A5038067008 @default.
- W4312014315 creator A5065443361 @default.
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- W4312014315 date "2021-12-11" @default.
- W4312014315 modified "2023-09-26" @default.
- W4312014315 title "Deep Translation Prior: Test-time Training for Photorealistic Style Transfer" @default.
- W4312014315 doi "https://doi.org/10.48550/arxiv.2112.06150" @default.
- W4312014315 hasPublicationYear "2021" @default.
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