Truong, G.Le, H.Zhang, E.Suter, D.Gilani, S.Z.2023-10-252023-10-252023IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023; 45(3):3890-39030162-88281939-3539https://hdl.handle.net/2440/139772Robust model fitting is a core algorithm in several computer vision applications. Despite being studied for decades, solving this problem efficiently for datasets that are heavily contaminated by outliers is still challenging: due to the underlying computational complexity. A recent focus has been on learning-based algorithms. However, most of these 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 (without labelled data) solve robust model fitting. Moreover, unlike other learning-based 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 (un)supervised learning approaches, and also achieves competitive results compared to traditional (non-learning-based) methods. Our approach is designed to try to maximise consensus (MaxCon), similar to the popular RANSAC. The basis of our approach, is to adopt a Reinforcement Learning framework. This requires designing appropriate reward functions, and state encodings. We provide a family of reward functions, tunable by choice of a parameter. We also investigate the application of different basic and enhanced Q-learning components.en© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.Maximum consensus; robust fitting; reinforcement learningJournal article10.1109/TPAMI.2022.31784422023-10-25638877Suter, D. [0000-0001-6306-3023]