论文标题
深层神经网络的主动学习的以模型为中心和数据的方面
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks
论文作者
论文摘要
我们以一致和统一的方式研究了通过深层神经网络的主动学习的不同方面。 i）我们研究了增量和累积培训模式，这些培训模式指定了新标记的数据用于培训。 ii）我们研究主动学习W.R.T.模型配置，例如时期和神经元的数量以及批处理大小的选择。 iii）我们详细考虑查询策略及其相应的信息性措施的行为，并因此提出了更有效的查询程序。 iv）我们对积极学习的类和测试误差估计进行统计分析，这些分析揭示了有关主动学习的一些见解。 v）我们研究神经网络的积极学习如何从伪标签中受益，作为实际标签的代理。
We study different aspects of active learning with deep neural networks in a consistent and unified way. i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training. ii) We study active learning w.r.t. the model configurations such as the number of epochs and neurons as well as the choice of batch size. iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning. v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.
