Domain adaptation (DA) aims to transfer knowledge from a fully labeled source
to a scarcely labeled or totally unlabeled target under domain shift. Recently,
semi-supervised learning-based (SSL) techniques that leverage pseudo labeling
have been increasingly used in DA. Despite the competitive performance, these
pseudo labeling methods rely heavily on the source domain to generate pseudo
labels for the target domain and therefore still suffer considerably from
source data bias. Moreover, class distribution bias in the target domain is
also often ignored in the pseudo label generation and thus leading to further
deterioration of performance. In this paper, we propose GeT that learns a
non-bias target embedding distribution with high quality pseudo labels.
Specifically, we formulate an online target generative classifier to induce the
target distribution into distinctive Gaussian components weighted by their
class priors to mitigate source data bias and enhance target class
discriminability. We further propose a structure similarity regularization
framework to alleviate target class distribution bias and further improve
target class discriminability. Experimental results show that our proposed GeT
is effective and achieves consistent improvements under various DA settings
with and without class distribution bias. Our code is available at:
this https URL

本文提出了一种名为 GeT 的方法，通过学习高质量的伪标签来学习无偏的目标嵌入分布，减轻源数据偏差并增强目标类别可辨识性，进一步通过结构相似性正则化框架缓解目标类别分布偏差并提高目标类别可辨识性，实验证明我们的方法在各种领域适应设置下均有效且有一致的改进。