Domain adaptation (DA) aims to transfer knowledge learned from a labeled source domain to an unlabeled or a less labeled but related target domain. Ideally, the source and target distributions should be aligned to each other equally to achieve unbiased knowledge transfer. However, due to the significant imbalance between the amount of annotated data in the source and target domains, usually only the target distribution is aligned to the source domain, leading to adapting unnecessary source specific knowledge to the target domain, i.e., biased domain adaptation. To resolve this problem, in this work, we delve into the transferability estimation problem in domain adaptation and propose a non-intrusive Unbiased Transferability Estimation Plug-in (UTEP) by modeling the uncertainty of a discriminator in adversarial-based DA methods to optimize unbiased transfer. We theoretically analyze the effectiveness of the proposed approach to unbiased transferability learning in DA. Furthermore, to alleviate the impact of imbalanced annotated data, we utilize the estimated uncertainty for pseudo label selection of unlabeled samples in the target domain, which helps achieve better marginal and conditional distribution alignments between domains. Extensive experimental results on a high variety of DA benchmark datasets show that the proposed approach can be readily incorporated into various adversarial-based DA methods, achieving state-of-the-art performance.
翻译:域适应(DA)的目的是将从标签源域到没有标签或标签较少但相关的目标领域所学的知识转移到没有标签或标签较少的目标领域; 理想的情况是,源和目标分布应彼此对齐,以便实现无偏见的知识转让; 然而,由于源域和目标领域附加说明的数据数量之间严重不平衡,通常只有目标分布与源域一致,导致不必要的源特定知识适应目标领域,即有偏见的领域适应。为了解决这个问题,我们在这项工作中深入到域适应中的可转让性估计问题,并提出一个无侵犯性、无偏见的可转让性估计插图(UTEP),通过模拟以对抗性、基于DA方法的歧视者不确定性,优化无偏见的转让; 我们从理论上分析拟议在DA中进行不偏不倚性转让学习的方法的有效性。 此外,为了减轻附加说明性数据不平衡的影响,我们利用在目标领域选择无标签的样本方面估计的不确定性,这有助于在区域间实现更好的边际和有条件的可转让性分布。 广泛实验性结果将DA的高级性基准方法纳入拟议的DA的高级性数据。