  We contribute to the sparsely populated area of unsupervised deep graph
matching with application to keypoint matching in images. Contrary to the
standard \emph{supervised} approach, our method does not require ground truth
correspondences between keypoint pairs. Instead, it is self-supervised by
enforcing consistency of matchings between images of the same object category.
As the matching and the consistency loss are discrete, their derivatives cannot
be straightforwardly used for learning. We address this issue in a principled
way by building our method upon the recent results on black-box differentiation
of combinatorial solvers. This makes our method exceptionally flexible, as it
is compatible with arbitrary network architectures and combinatorial solvers.
Our experimental evaluation suggests that our technique sets a new
state-of-the-art for unsupervised graph matching.
