论文标题
本地上下文感知的活动域适应
Local Context-Aware Active Domain Adaptation
论文作者
论文摘要
Active域Adaptation（ADA）查询少数选定目标样本的标签，以帮助将模型从源域调整到目标域。查询数据的局部环境很重要，尤其是当域间隙较大时。但是，现有的ADA作品尚未完全探讨这一点。在本文中，我们提出了一个名为LADA的本地环境感知的ADA框架，以解决此问题。为了选择信息性的目标样本，我们根据模型预测的局部不一致设计了一种新的标准。由于标签预算通常很小，因此仅查询数据的微调模型效率低下。我们以阶级平衡的方式逐步与自信的邻居一起逐步增强标记目标数据。实验验证了所提出的标准比现有的主动选择策略选择了更多信息的目标样本。此外，我们的完整方法清楚地超过了各种基准的ADA艺术。代码可在https://github.com/tsun/lada上找到。
Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context of queried data is important, especially when the domain gap is large. However, this has not been fully explored by existing ADA works. In this paper, we propose a Local context-aware ADA framework, named LADA, to address this issue. To select informative target samples, we devise a novel criterion based on the local inconsistency of model predictions. Since the labeling budget is usually small, fine-tuning model on only queried data can be inefficient. We progressively augment labeled target data with the confident neighbors in a class-balanced manner. Experiments validate that the proposed criterion chooses more informative target samples than existing active selection strategies. Furthermore, our full method clearly surpasses recent ADA arts on various benchmarks. Code is available at https://github.com/tsun/LADA.
