论文标题
学习为主动学习排名：一种列表方法
Learning to Rank for Active Learning: A Listwise Approach
论文作者
论文摘要
积极学习是一种减轻努力为饥饿应用程序（例如图像/视频索引和检索，自主驾驶等）标记大量数据的努力的替代方法。积极学习的目的是根据采集功能自动选择许多未标记的样本进行注释（根据预算），这表明样本对于培训模型的价值。学习损失方法是一种任务不合时宜的方法，它附上一个模块，以学习预测未标记数据的目标丢失，并选择标签损失最高的数据。在这项工作中，我们遵循此策略，但我们将采集功能定义为学习问题并使用简单但有效的列表方法来重新考虑损失预测模块的结构。四个数据集的实验结果表明，对于图像分类和回归任务，我们的方法的表现优于最新的主动学习方法。
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks.
