论文标题
半监督的批处理主动学习通过二线优化
Semi-supervised Batch Active Learning via Bilevel Optimization
论文作者
论文摘要
主动学习是通过提高数据效率来降低标签成本的有效技术。在这项工作中，我们提出了一种新颖的批次获取策略，用于在以半监督方式进行模型训练的环境中进行积极学习。我们通过二线优化将方法作为数据摘要问题提出方法，其中查询批次由最能汇总未标记数据池的点组成。我们表明，当仅少量标记样本可用时，我们的方法在制度中的关键字检测任务中非常有效。
Active learning is an effective technique for reducing the labeling cost by improving data efficiency. In this work, we propose a novel batch acquisition strategy for active learning in the setting where the model training is performed in a semi-supervised manner. We formulate our approach as a data summarization problem via bilevel optimization, where the queried batch consists of the points that best summarize the unlabeled data pool. We show that our method is highly effective in keyword detection tasks in the regime when only few labeled samples are available.
