Random sample consensus (RANSAC), which is based on a repetitive sampling from a given dataset, is one of the most popular robust estimation methods. In this study, an energy-based model (EBM) for robust estimation that has a similar scheme to RANSAC, energy-based RANSAC (EB-RANSAC), is proposed. EB-RANSAC is applicable to a wide range of estimation problems similar to RANSAC. However, unlike RANSAC, EB-RANSAC does not require a troublesome sampling procedure and has only one hyperparameter. The effectiveness of EB-RANSAC is numerically demonstrated in two applications: a linear regression and maximum likelihood estimation.
@article{arxiv.2603.12525,
title = {EB-RANSAC: Random Sample Consensus based on Energy-Based Model},
author = {Muneki Yasuda and Nao Watanabe and Kaiji Sekimoto},
journal= {arXiv preprint arXiv:2603.12525},
year = {2026}
}