  Convolutional neural networks (CNNs) have been successfully applied to solve
the problem of correspondence estimation between semantically related images.
Due to non-availability of large training datasets, existing methods resort to
self-supervised or unsupervised training paradigm. In this paper we propose a
semi-supervised learning framework that imposes cyclic consistency constraint
on unlabeled image pairs. Together with the supervised loss the proposed model
achieves state-of-the-art on a benchmark semantic matching dataset.
