Robust model fitting is a core algorithm in a large number of computer vision
applications. Solving this problem efficiently for datasets highly contaminated
with outliers is, however, still challenging due to the underlying
computational complexity. Recent literature has focused on learning-based
algorithms. However, most approaches are supervised which require a large
amount of labelled training data. In this paper, we introduce a novel
unsupervised learning framework that learns to directly solve robust model
fitting. Unlike other methods, our work is agnostic to the underlying input
features, and can be easily generalized to a wide variety of LP-type problems
with quasi-convex residuals. We empirically show that our method outperforms
existing unsupervised learning approaches, and achieves competitive results
compared to traditional methods on several important computer vision problems.

本文提出了一种新颖的无监督学习框架，能够直接解决鲁棒模型拟合问题，相比之前的无监督学习方法，本方法在多个重要的计算机视觉问题上表现出色，并与传统方法取得了有竞争力的结果。