  Object segmentation is a crucial problem that is usually solved by using
supervised learning approaches over very large datasets composed of both images
and corresponding object masks. Since the masks have to be provided at pixel
level, building such a dataset for any new domain can be very time-consuming.
We present ReDO, a new model able to extract objects from images without any
annotation in an unsupervised way. It relies on the idea that it should be
possible to change the textures or colors of the objects without changing the
overall distribution of the dataset. Following this assumption, our approach is
based on an adversarial architecture where the generator is guided by an input
sample: given an image, it extracts the object mask, then redraws a new object
at the same location. The generator is controlled by a discriminator that
ensures that the distribution of generated images is aligned to the original
one. We experiment with this method on different datasets and demonstrate the
good quality of extracted masks.
