  Conventional unsupervised domain adaptation (UDA) methods need to access both
labeled source samples and unlabeled target samples simultaneously to train the
model. While in some scenarios, the source samples are not available for the
target domain due to data privacy and safety. To overcome this challenge,
recently, source-free domain adaptation (SFDA) has attracted the attention of
researchers, where both a trained source model and unlabeled target samples are
given. Existing SFDA methods either adopt a pseudo-label based strategy or
generate more samples. However, these methods do not explicitly reduce the
distribution shift across domains, which is the key to a good adaptation.
Although there are no source samples available, fortunately, we find that some
target samples are very similar to the source domain and can be used to
approximate the source domain. This approximated domain is denoted as the
pseudo-source domain. In this paper, inspired by this observation, we propose a
novel method based on the pseudo-source domain. The proposed method firstly
generates and augments the pseudo-source domain, and then employs distribution
alignment with four novel losses based on pseudo-label based strategy. Among
them, a domain adversarial loss is introduced between the pseudo-source domain
the remaining target domain to reduce the distribution shift. The results on
three real-world datasets verify the effectiveness of the proposed method.
