Reviews: Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks

Summary: The paper presents a semi-supervised approach to learning optical flow using a generative adversarial network (GAN) on flow warp errors. Rather than using a handcrafted loss (e.g., deviation of brightness constancy deviation from smoothness) the paper explores the use of a GAN applied to flow warp errors. Strengths: novel semi-supervised approach to learning; some concerns on the novelty in the light of [21] generally written well Weaknesses: - some key evaluations missing Comments: Supervised (e.g., [8]) and unsupervised (e.g., [39]) approaches to optical flow prediction have previously been investigated, the type of semi-supervised supervision proposed here appears novel. The main contribution is in the introduction of an adversarial loss for training rather than the particulars of the flow prediction architecture. As discussed in Sec. 2, [21] also proposes an adversarial scheme.