Domain adaptation (DA) aims to transfer the knowledge learned from a source
domain to an unlabeled target domain. Some recent works tackle source-free
domain adaptation (SFDA) where only a source pre-trained model is available for
adaptation to the target domain. However, those methods do not consider keeping
source performance which is of high practical value in real world applications.
In this paper, we propose a new domain adaptation paradigm called Generalized
Source-free Domain Adaptation (G-SFDA), where the learned model needs to
perform well on both the target and source domains, with only access to current
unlabeled target data during adaptation. First, we propose local structure
clustering (LSC), aiming to cluster the target features with its semantically
similar neighbors, which successfully adapts the model to the target domain in
the absence of source data. Second, we propose sparse domain attention (SDA),
it produces a binary domain specific attention to activate different feature
channels for different domains, meanwhile the domain attention will be utilized
to regularize the gradient during adaptation to keep source information. In the
experiments, for target performance our method is on par with or better than
existing DA and SFDA methods, specifically it achieves state-of-the-art
performance (85.4%) on VisDA, and our method works well for all domains after
adapting to single or multiple target domains. Code is available in
this https URL

本文提出了一种广义源自由域自适应（G-SFDA）的新域自适应范例，其中仅在自适应期间访问当前未标记的目标数据，该方法基于局部结构聚类（LSC）和稀疏域注意力（SDA）以保持源信息的同时为不同领域激活不同的特征通道。实验表明，本文所提出的方法的目标表现与现有域自适应（DA）和源自由域自适应（SFDA）方法相当或更好，并在 VisDA 上实现了最先进的性能（85.4％）。