Instance Normalization: The Missing Ingredient for Fast Stylization

This paper revisits a previously introduced fast stylization method and shows that a small change to the stylization architecture produces a significant qualitative improvement in the generated images. The change is deliberately minimal: it swaps batch normalization for instance normalization, and importantly applies instance normalization at both training and testing times.

With this modification, the method can be used to train high-performance architectures for real-time image generation, improving output quality without added architectural complexity. The authors note that the code is made available publicly, allowing others to reproduce and build on the fast stylization results.