  Deep neural networks trained to inpaint partially occluded images show a deep
understanding of image composition and have even been shown to remove objects
from images convincingly. In this work, we investigate how this implicit
knowledge of image composition can be leveraged for fully self-supervised
instance separation. We propose a measure for the independence of two image
regions given a fully self-supervised inpainting network and separate objects
by maximizing this independence. We evaluate our method on two microscopy image
datasets and show that it reaches similar segmentation performance to fully
supervised methods.
