Enhanced Class-Specific Spatial Normalization for Image Generation

We propose an 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 spatial-class map can be efficiently utilized with our proposed method, which results in competing performances with much fewer parameters.