LAUSR

We propose enhanced class-specific spatial normalization, a simple yet effective layer to generate a photorealistic image given a spatial-class map. Under the assumption that pixels belonging to the same class share the same distribution in the feature space, we intuitively split an image into classes according to the map. By learning the class-specific distributions, our generator can distinguish one class from other classes. Further, our spatial normalization combines the spatial-class map and the class-specific distributions, by which our generator can produce instances in the desired locations. We apply the proposed normalization not only in semantic image generation but also in object transfiguration. The experimental results demonstrate that the proposed method encourages neural networks to use the spatial-class map efficiently with competing performances, yet much fewer parameters.