Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned
from a labeled source dataset to solve similar tasks in a new unlabeled domain.
Prior UDA methods typically require to access the source data when learning to
adapt the model, making them risky and inefficient for decentralized private
data. This work tackles a practical setting where only a trained source model
is available and investigates how we can effectively utilize such a model
without source data to solve UDA problems. We propose a simple yet generic
representation learning framework, named \emph{Source HypOthesis Transfer}
(SHOT). SHOT freezes the classifier module (hypothesis) of the source model and
learns the target-specific feature extraction module by exploiting both
information maximization and self-supervised pseudo-labeling to implicitly
align representations from the target domains to the source hypothesis. To
verify its versatility, we evaluate SHOT in a variety of adaptation cases
including closed-set, partial-set, and open-set domain adaptation. Experiments
indicate that SHOT yields state-of-the-art results among multiple domain
adaptation benchmarks.

本文提出一种基于源模型的无监督域自适应解决方案 SHOTS，在缺乏源数据的情况下，通过信息最大化和自监督伪标签相结合的方式，学习目标特定的特征提取模块，从而使目标域的表示与源假设对齐，并在多个域自适应基准测试中取得了最新的成果。