We study different data-centric and model-centric aspects of active learning with neural network models. i) We investigate incremental and cumulative training modes that specify how the currently labeled data are used for training. ii) Neural networks are models with a large capacity. Thus, we study how active learning depends on the number of epochs and neurons as well as the choice of batch size. iii) We analyze in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying and active learning paradigms. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning.
翻译:我们用神经网络模型研究以数据为中心的和以模式为中心的积极学习的不同方面。 (一) 我们调查说明目前标记的数据如何用于培训的渐进式和累积式培训模式。 (二) 神经网络是具有巨大容量的模型。 因此,我们研究积极学习如何取决于时代和神经元的数量以及批量大小的选择。 (三) 我们详细分析查询战略的行为及其相应的信息量度措施,并据此提出更有效率的查询和积极学习模式。 (四) 我们进行统计分析,例如积极学习班级和测试错误估计,这些分析揭示了对积极学习的一些洞察力。