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Research map / Image generation / Editing and style transfer

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

ICML 2016 · Image generation › Editing and style transfer · approach: Arbitrary neural style transfer · 599 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace iterative optimization with a feed-forward generator network for texture synthesistexture synthesis · texture synthesis vs Gatys et al.generation speed: 500x fasterHelped
feed-forward generation instead of backprop-based optimizationtexture synthesis · 256x256 samplesmemory use: 170 MB vs 1100 MBHelped
multi-scale generator architecture instead of simple chain of conv/upsampling layers (ablation)generator network · texture synthesistexture loss and perceptual qualityHelped
batch normalization inserted before concatenation layers (ablation)generator network · texture trainingtraining qualityHelped
strided full-convolution upsampling instead of nearest-neighbour upsampling (ablation)generator network · texture synthesisvisual qualityHurt
train style transfer on a small pool of about 16 content images instead of many (ablation)style transfer generator · stylizationqualitative result qualityHelped
scale the input noise magnitude at test timestyle transfer generator · stylizationstyle/content balanceHelped
feed-forward stylization instead of optimization-based style transferstyle transfer · stylization vs Gatys et al.perceptual qualityMixed

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