论文标题
主动替代估计器：一种用于标签高效模型评估的主动学习方法
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
论文作者
论文摘要
我们提出了主动替代估计器（ASE），这是一种用于标签有效模型评估的新方法。当标签价格昂贵时，评估模型性能是一个具有挑战性且重要的问题。 ASE使用基于替代物的估计方法来解决此主动测试问题，该方法可以用未知标签的点插入点误差，而不是形成蒙特卡洛估计器。 Ases积极学习基本的代孕，我们提出了一种新颖的采集策略XWED，该策略为最终的估计任务量身定制了这一学习。我们发现，与当前的最新技术评估问题相比，ASE提供的标签效率高于当前的最新标签效率。
We propose Active Surrogate Estimators (ASEs), a new method for label-efficient model evaluation. Evaluating model performance is a challenging and important problem when labels are expensive. ASEs address this active testing problem using a surrogate-based estimation approach that interpolates the errors of points with unknown labels, rather than forming a Monte Carlo estimator. ASEs actively learn the underlying surrogate, and we propose a novel acquisition strategy, XWED, that tailors this learning to the final estimation task. We find that ASEs offer greater label-efficiency than the current state-of-the-art when applied to challenging model evaluation problems for deep neural networks.
