Video stabilization is the process of eliminating unwanted camera movements and shaking in a recorded video. Recently, learning-based video stabilization methods have become very popular. Supervised learning-based approaches need labeled data. For the video stabilization problem, recording both stable and unstable versions of the same video is quite troublesome and requires special hardware. In order to overcome this situation, learning-based interpolation methods that do not need such data have been proposed. In this paper, we review recent learning-based interpolation methods for video stabilization and discuss the shortcomings and potential improvements of them.