  We study a practical domain adaptation task, called source-free unsupervised
domain adaptation (UDA) problem, in which we cannot access source domain data
due to data privacy issues but only a pre-trained source model and unlabeled
target data are available. This task, however, is very difficult due to one key
challenge: the lack of source data and target domain labels makes model
adaptation very challenging. To address this, we propose to mine the hidden
knowledge in the source model and exploit it to generate source avatar
prototypes (i.e., representative features for each source class) as well as
target pseudo labels for domain alignment. To this end, we propose a
Contrastive Prototype Generation and Adaptation (CPGA) method. Specifically,
CPGA consists of two stages: (1) prototype generation: by exploring the
classification boundary information of the source model, we train a prototype
generator to generate avatar prototypes via contrastive learning. (2) prototype
adaptation: based on the generated source prototypes and target pseudo labels,
we develop a new robust contrastive prototype adaptation strategy to align each
pseudo-labeled target data to the corresponding source prototypes. Extensive
experiments on three UDA benchmark datasets demonstrate the effectiveness and
superiority of the proposed method.
