Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns. This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to, the source data. To effectively utilize the source model for adaptation, we propose a novel approach called Source HypOthesis Transfer (SHOT), which learns the feature extraction module for the target domain by fitting the target data features to the frozen source classification module (representing classification hypothesis). Specifically, SHOT exploits both information maximization and self-supervised learning for the feature extraction module learning to ensure the target features are implicitly aligned with the features of unseen source data via the same hypothesis. Furthermore, we propose a new labeling transfer strategy, which separates the target data into two splits based on the confidence of predictions (labeling information), and then employ semi-supervised learning to improve the accuracy of less-confident predictions in the target domain. We denote labeling transfer as SHOT++ if the predictions are obtained by SHOT. Extensive experiments on both digit classification and object recognition tasks show that SHOT and SHOT++ achieve results surpassing or comparable to the state-of-the-arts, demonstrating the effectiveness of our approaches for various visual domain adaptation problems. Code will be available at \url{https://github.com/tim-learn/SHOT-plus}.
翻译:未经监督的域适应(UDA)旨在将知识从一个相关但标签良好的不同源域域转移到一个新的未标注的目标域。多数现有的UDA方法需要访问源数据,因此当数据保密且由于隐私问题无法共享时,不适用。本文旨在解决现实的设置,只有经过培训的分类模型,而不是访问源数据。为了有效利用源的适应模式,我们提议了一个新颖的方法,即源 HypOthesis 传输(SHOT),该方法通过将目标域的目标数据特性与冻结源分类模块(代表分类假设)相匹配,学习目标域的特征提取模块的功能模块。具体地说,SHOT利用信息最大化和自我监督学习功能提取模块学习,以确保目标特征通过同一假设与隐性源数据特征相一致。此外,我们提出了一个新的标签传输战略,根据各种预测的可信度(标签信息)将目标数据分为两个分裂部分,然后使用半超强的学习方法来提高目标域域目标域的目标数据提取的特性,如果SHOOO-S在目标域域的预测中显示比较性结果,那么SHOOT将显示,SHOT的升级的定位将显示S-S-dlOB的升级的标签将显示S-s。