Instance Normalization: The Missing Ingredient for Fast Stylization
arXiv e-Print archive - 2016 via Local arXiv
First published: 2016/07/27 (4 years ago) Abstract: 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.
In the context of stylization, Ulyanov et al. propose to use instance normalization instead of batch normalization. In detail, instance normalization does not compute the mean and standard deviation used for normalization over the current mini-batch in training. Instead, these statistics are computed per instance individually. This also has the benefit of having the same training and test procedure, meaning that normalization is the same in both cases – in contrast to batch normalization.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).