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Research map / Image generation / Editing and style transfer

StyTr2: Image Style Transfer With Transformers

CVPR 2022 · Image generation › Editing and style transfer · approach: Arbitrary neural style transfer

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a transformer-based style-transfer architecture instead of CNN-based encoder-decoder methodsStyTr2 · Repeated stylizationcontent structure preservationHelped
Use content-aware, scale-invariant positional encoding instead of sinusoidal positional encoding (ablation)StyTr2 · Different input resolutions and repetitive-pattern contentvisual qualityHelped
Use content-aware positional encoding instead of sinusoidal positional encoding (ablation)StyTr2 · Repetitive-pattern content imagesconsistency of stylized regionsHelped
Use a transformer architecture rather than a CNN-based encoder-decoder pipelineStyTr2 · MS-COCO and WikiArt; 800 stylized imagescontent loss (Lc): 1.91 vs. 1.97Helped
Use StyTr2 rather than SANetImage style transfer · MS-COCO and WikiArt; 800 stylized imagesstyle loss (Ls): 1.47 vs. 1.18Hurt
Use StyTr2 rather than IESTImage style transfer · MS-COCO and WikiArt; 800 stylized imagescontent loss (Lc): 1.97 -> 1.91Helped
Use StyTr2 rather than AdaINImage style transfer · MS-COCO and WikiArt; 800 stylized imagescontent loss (Lc): 2.34 -> 1.91Helped
Use StyTr2 rather than AdaINImage style transfer · MS-COCO and WikiArt; 800 stylized imagesstyle loss (Ls): 1.91 -> 1.47Helped

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