Papers › Instance Normalization: The Missing Ingredient for Fast Stylization

Instance Normalization: The Missing Ingredient for Fast Stylization

27 Jul 2016arXiv:1607.08022archive 2025-07-28

Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky

It this paper we revisit the fast stylization method introduced in Ulyanov et. al. (2016). We show how a small change in the stylization architecture results in a significant qualitative improvement in the generated images. The change is limited to swapping batch normalization with instance normalization, and to apply the latter both at training and testing times. The resulting method can be used to train high-performance architectures for real-time image generation. The code will is made available on github at https://github.com/DmitryUlyanov/texture_nets. Full paper can be found at arXiv:1701.02096.

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DmitryUlyanov/texture_nets officialmentioned in papertorchApache-2.0 report
Chad4545/styletransfer mentioned on GitHubpytorch report
Net-Mist/style-transfer-tf2 mentioned on GitHubtfMIT report
aryan-mann/style-transfer mentioned on GitHub report
brightyoun/Video-Style-Transfer mentioned on GitHubpytorchMIT report
buddly27/stylish mentioned on GitHub report
cryu854/FastStyle mentioned on GitHubtf report
cryu854/SinGAN mentioned on GitHubtf report
etttttte/mayfest2018 mentioned on GitHubpytorch report
jcjohnson/fast-neural-style mentioned on GitHubtorch report
lengstrom/fast-style-transfer mentioned on GitHubtf report
riven314/PerceptualLoss-FastAI mentioned on GitHubpytorch report
sundogai/style-transfer mentioned on GitHubtf report
tbullmann/imagetranslation-tensorflow mentioned on GitHubtfNOASSERTION report
the-super-toys/glimpse-models mentioned on GitHubpytorch report
yakhyo/Fast-Neural-Style-Transfer mentioned on GitHubpytorch report

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gram_matrix brightyoun/Video-Style-Transfer/utils.py community (archive-listed) ran · our draft was wrong fingerprinted MIT (permissive) · 430c582cd8bbb1df · report
abs_criterion Net-Mist/style-transfer-tf2/src/losses.py community (archive-listed) unverified MIT (permissive) · 54e8627bef847403 · report
bytes_feature Net-Mist/style-transfer-tf2/src/tfrecord.py community (archive-listed) unverified MIT (permissive) · d682a9593710857e · report
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int64_feature Net-Mist/style-transfer-tf2/src/tfrecord.py community (archive-listed) unverified MIT (permissive) · 328d17201d9a0e68 · report
int64_list_feature Net-Mist/style-transfer-tf2/src/tfrecord.py community (archive-listed) unverified MIT (permissive) · f6d735872f4495ed · report
inverted_res_block Net-Mist/style-transfer-tf2/src/model.py community (archive-listed) unverified MIT (permissive) · f169004dc49d5433 · report
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process_config hollygrimm/cyclegan-keras-art-attrs/utils/utils.py community (archive-listed) unverified MIT (permissive) · 7081cbaca5dca320 · report
relu6 Net-Mist/style-transfer-tf2/src/model.py community (archive-listed) unverified MIT (permissive) · 4ab5b6864f23abf4 · report
stylize_onnx_caffe2 brightyoun/Video-Style-Transfer/neural_style.py community (archive-listed) unverified MIT (permissive) · 33bdd8217a2859e8 · report

Tasks

Image GenerationImage StylizationStyle Transfer

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Methods

Introduced by this paper: Instance Normalization

Batch NormalizationInstance Normalization

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