  In this paper we address the problem of establishing correspondences between
different instances of the same object. The problem is posed as finding the
geometric transformation that aligns a given image pair. We use a convolutional
neural network (CNN) to directly regress the parameters of the transformation
model. The alignment problem is defined in the setting where an unordered set
of semantic key-points per image are available, but, without the correspondence
information. To this end we propose a novel loss function based on cyclic
consistency that solves this 2D point set registration problem by inferring the
optimal geometric transformation model parameters. We train and test our
approach on a standard benchmark dataset Proposal-Flow
(PF-PASCAL)\cite{proposal_flow}. The proposed approach achieves
state-of-the-art results demonstrating the effectiveness of the method. In
addition, we show our approach further benefits from additional training
samples in PF-PASCAL generated by using category level information.
