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Instance Normalization: The Missing Ingredient for Fast Stylization

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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.

Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky• 2016

Related benchmarks

TaskDatasetResultRank
Image ClassificationImageNet (val)
Top-1 Acc73.93
1206
Graph ClassificationMutag (test)
Accuracy90.5
217
Graph ClassificationPROTEINS (test)
Accuracy76.5
180
Graph ClassificationNCI1 (test)
Accuracy0.812
174
Graph RegressionZINC 12K (test)
MAE0.2984
164
Graph ClassificationIMDB-B (test)
Accuracy74.8
134
Graph ClassificationCOLLAB (test)
Accuracy80
96
Graph ClassificationMolHIV
ROC AUC76.88
82
Image ClassificationImageNet (val)
Top-1 Error28.4
72
Graph ClassificationPTC (test)
Accuracy64.7
49
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