EB-RANSAC: Random Sample Consensus based on Energy-Based Model

Fuente: arXiv
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Main Authors: Yasuda, Muneki, Watanabe, Nao, Sekimoto, Kaiji
Format: Preprint
Published: 2026
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author Yasuda, Muneki
Watanabe, Nao
Sekimoto, Kaiji
author_facet Yasuda, Muneki
Watanabe, Nao
Sekimoto, Kaiji
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_12525
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle EB-RANSAC: Random Sample Consensus based on Energy-Based Model
Yasuda, Muneki
Watanabe, Nao
Sekimoto, Kaiji
Machine Learning
Disordered Systems and Neural Networks
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.
title EB-RANSAC: Random Sample Consensus based on Energy-Based Model
topic Machine Learning
Disordered Systems and Neural Networks
url https://arxiv.org/abs/2603.12525