  Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer
knowledge from a well-trained source model to a related but unlabeled target
domain. In such a scenario, all conventional adaptation methods that require
source data fail. To combat this challenge, existing USFDAs turn to transfer
knowledge by aligning the target feature to the latent distribution hidden in
the source model. However, such information is naturally limited. Thus, the
alignment in such a scenario is not only difficult but also insufficient, which
degrades the target generalization performance. To relieve this dilemma in
current USFDAs, we are motivated to explore a new perspective to boost their
performance. For this purpose and gaining necessary insight, we look back upon
the origin of the domain adaptation and first theoretically derive a new-brand
target generalization error bound based on the model smoothness. Then,
following the theoretical insight, a general and model-smoothness-guided
Jacobian norm (JN) regularizer is designed and imposed on the target domain to
mitigate this dilemma. Extensive experiments are conducted to validate its
effectiveness. In its implementation, just with a few lines of codes added to
the existing USFDAs, we achieve superior results on various benchmark datasets.
