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 is available at
https://github.com/tim-learn/SHOT-plus.

本文针对数据保护的要求，提出了一种基于分类模型训练的无监督领域自适应方法：Source HypOthesis Transfer (简称 SHOT)，通过提取目标领域的特征并利用同一假设对原数据和目标数据进行映射，同时利用半监督学习来提高目标域中置信度较低的样本的准确性，实现了卓越的性能及实用价值。

基于假设传递和标签传递的源数据缺失无监督域自适应

Source Data-absent Unsupervised Domain Adaptation through Hypothesis  Transfer and Labeling Transfer

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。