  Recently, learning-based image synthesis has enabled to generate
high-resolution images, either applying popular adversarial training or a
powerful perceptual loss. However, it remains challenging to successfully
leverage synthetic data for improving semantic segmentation with additional
synthetic images. Therefore, we suggest to generate intermediate convolutional
features and propose the first synthesis approach that is catered to such
intermediate convolutional features. This allows us to generate new features
from label masks and include them successfully into the training procedure in
order to improve the performance of semantic segmentation. Experimental results
and analysis on two challenging datasets Cityscapes and ADE20K show that our
generated feature improves performance on segmentation tasks.
