用能量模型替代传统采样，让鲁棒估计更高效。
EB-RANSAC: Random Sample Consensus based on Energy-Based Model
- 用能量模型替代重复采样，简化鲁棒估计流程
- 仅需一个超参数，且在两类问题上验证有效
- 适合需要高效鲁棒估计的工程与科研场景
随机采样一致（RANSAC）是一种基于从数据集中重复采样的流行鲁棒估计方法。本文提出一种基于能量模型（EBM）的鲁棒估计框架——能量基RANSAC（EB-RANSAC），其结构与RANSAC相似，但无需繁琐的采样过程，且仅有一个超参数。该方法适用于多种类似RANSAC的估计任务。通过线性回归和最大似然估计两个应用，数值实验验证了其有效性。
原文摘要 · Abstract (English)
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.
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