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 be made available at https://github.com/DmitryUlyanov/texture_nets.
The optimization proposed by Ulyanov et al. REF also showed that instance normalization could be applied to the CNN with improved results over batch normalization, in training and testing time.
Instance Normalization: The Missing Ingredient for Fast Stylization
ArXiv
ArXiv
Computer Science