In multi-domain learning (MDL) scenarios, high labeling effort is required
due to the complexity of collecting data from various domains. Active Learning
(AL) presents an encouraging solution to this issue by annotating a smaller
number of highly informative instances, thereby reducing the labeling effort.
Previous research has relied on conventional AL strategies for MDL scenarios,
which underutilize the domain-shared information of each instance during the
selection procedure. To mitigate this issue, we propose a novel
perturbation-based two-stage multi-domain active learning (P2S-MDAL) method
incorporated into the well-regarded ASP-MTL model. Specifically, P2S-MDAL
involves allocating budgets for domains and establishing regions for diversity
selection, which are further used to select the most cross-domain influential
samples in each region. A perturbation metric has been introduced to evaluate
the robustness of the shared feature extractor of the model, facilitating the
identification of potentially cross-domain influential samples. Experiments are
conducted on three real-world datasets, encompassing both texts and images. The
superior performance over conventional AL strategies shows the effectiveness of
the proposed strategy. Additionally, an ablation study has been carried out to
demonstrate the validity of each component. Finally, we outline several
intriguing potential directions for future MDAL research, thus catalyzing the
field's advancement.

本研究提出了一种新颖的基于扰动的两阶段多域主动学习（P2S-MDAL）方法，它包括分配域的预算、建立多样性选择的区域，并进一步用于选择每个区域中最具交叉域影响力的样本。在三个现实世界数据集上进行的实验表明，与传统的 AL 策略相比，该方法具有优越的性能。