Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit free energy biases when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of targe data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https://github.com/BIT-DA/EADA.

本文介绍了一种基于能量模型的主动学习策略，名为“EADA”，可用于帮助将深度神经网络推广到新的目标域。该算法通过求解目标数据组的自由能量，结合领域特征和实例不确定性，从中选择最有价值的目标数据样本。此外，领域差可以通过调整目标数据的自由能量使之紧凑地围绕源域，从而得到有效缩小。实验表明，该算法在各项基准测试中表现良好，比其他先进方法有很大的优势。

基于能量的领域自适应主动学习