  We investigate how generated structures of GANs correlate with their
activations in hidden layers, with the purpose of better understanding the
inner workings of those models and being able to paint structures with
unconditionally trained GANs. This gives us more control over the generated
images, allowing to generate them from a semantic segmentation map while not
requiring such a segmentation in the training data. To this end we introduce
the concept of tileable features, allowing us to identify activations that work
well for painting.
