Optical flow is an important component in many computer vision applications. Thanks to deep learning, there have been great improvements in optical flow estimation in the past several years. But all of the top performing models are trained using a supervised method, on synthetic data sets. As the creation of accurately labeled optical flow data sets from real world images is incredibly difficult, many researchers have turned to developing unsupervised approaches. In this paper we conduct a survey of some of the most recent papers in unsupervised learning of optical flow, and present some of the key elements that are universally utilized. In addition, we did a results comparison, and found that the best performing unsupervised models are UnDAF-RAFT for the MPI-Sintel benchmark, and UpFlow on the KITTI benchmark. But both models still have considerably worse results when compared to supervised methods.