论文标题
积极学习，预期错误降低
Active Learning with Expected Error Reduction
论文作者
论文摘要
积极的学习已被广泛研究为有效数据收集的方法。在文献中的众多方法中，预期误差降低（EER）（Roy和McCallum）已被证明是一种有效学习的有效方法：选择候选样本，以期望在未标记的集合中最大程度地减少误差。但是，EER要求为每个候选样本进行重新训练，因此由于这种较大的计算成本，因此未广泛用于现代深层神经网络。在本文中，我们在贝叶斯主动学习的镜头下重新重新进行了EER，并得出了可以使用任何贝叶斯参数采样方法的计算高效版本（例如Arxiv：1506.02142）。然后，我们使用Monte Carlo辍学的方法比较了我们方法的经验性能，以针对深度活跃学习文献中最新方法进行参数采样。实验是在四个标准基准数据集和三个Wild数据集上进行的（ARXIV：2012.07421）。结果表明，我们的方法优于所有其他方法，除了数据移动方案中的一种方法：一种依赖模型的，非信息理论方法，该方法需要更高的计算成本（ARXIV：1906.03671）。
Active learning has been studied extensively as a method for efficient data collection. Among the many approaches in literature, Expected Error Reduction (EER) (Roy and McCallum) has been shown to be an effective method for active learning: select the candidate sample that, in expectation, maximally decreases the error on an unlabeled set. However, EER requires the model to be retrained for every candidate sample and thus has not been widely used for modern deep neural networks due to this large computational cost. In this paper we reformulate EER under the lens of Bayesian active learning and derive a computationally efficient version that can use any Bayesian parameter sampling method (such as arXiv:1506.02142). We then compare the empirical performance of our method using Monte Carlo dropout for parameter sampling against state of the art methods in the deep active learning literature. Experiments are performed on four standard benchmark datasets and three WILDS datasets (arXiv:2012.07421). The results indicate that our method outperforms all other methods except one in the data shift scenario: a model dependent, non-information theoretic method that requires an order of magnitude higher computational cost (arXiv:1906.03671).
