Improving performance in multiple domains is a challenging task, and often requires significant amounts of data to train and test models. Active learning techniques provide a promising solution by enabling models to select the most informative samples for labeling, thus reducing the amount of labeled data required to achieve high performance. In this paper, we present an active learning-based framework for improving performance across multiple domains. Our approach consists of two stages: first, we use an initial set of labeled data to train a base model, and then we iteratively select the most informative samples for labeling to refine the model. We evaluate our approach on several multi-domain datasets, including image classification, sentiment analysis, and object recognition. Our experiments demonstrate that our approach consistently outperforms baseline methods and achieves state-of-the-art performance on several datasets. We also show that our method is highly efficient, requiring significantly fewer labeled samples than other active learning-based methods. Overall, our approach provides a practical and effective solution for improving performance across multiple domains using active learning techniques.

翻译：跨多个领域提升性能是一项具有挑战性的任务，通常需要大量数据来训练和测试模型。主动学习技术通过允许模型选择最具信息量的样本进行标注，从而减少实现高性能所需的标注数据量，为这一问题提供了有前景的解决方案。本文提出了一种基于主动学习的框架，用于提升跨多个领域的性能。该方法包含两个阶段：首先，利用初始标注数据集训练基础模型；随后，迭代选择最具信息量的样本进行标注以优化模型。我们在包含图像分类、情感分析和目标识别的多个多领域数据集上评估了该方法。实验结果表明，该方法在多个数据集上始终优于基线方法，并达到了最先进的性能水平。此外，我们的方法具有高效性，所需标注样本数量显著少于其他基于主动学习的方法。总体而言，本文提出的方法为利用主动学习技术提升多领域性能提供了实用且有效的解决方案。