  Unsupervised domain adaptation (UDA) via deep learning has attracted
appealing attention for tackling domain-shift problems caused by distribution
discrepancy across different domains. Existing UDA approaches highly depend on
the accessibility of source domain data, which is usually limited in practical
scenarios due to privacy protection, data storage and transmission cost, and
computation burden. To tackle this issue, many source-free unsupervised domain
adaptation (SFUDA) methods have been proposed recently, which perform knowledge
transfer from a pre-trained source model to unlabeled target domain with source
data inaccessible. A comprehensive review of these works on SFUDA is of great
significance. In this paper, we provide a timely and systematic literature
review of existing SFUDA approaches from a technical perspective. Specifically,
we categorize current SFUDA studies into two groups, i.e., white-box SFUDA and
black-box SFUDA, and further divide them into finer subcategories based on
different learning strategies they use. We also investigate the challenges of
methods in each subcategory, discuss the advantages/disadvantages of white-box
and black-box SFUDA methods, conclude the commonly used benchmark datasets, and
summarize the popular techniques for improved generalizability of models
learned without using source data. We finally discuss several promising future
directions in this field.
