  Source-free Unsupervised Domain Adaptation (SF-UDA) aims to adapt a
well-trained source model to an unlabeled target domain without access to the
source data. One key challenge is the lack of source data during domain
adaptation. To handle this, we propose to mine the hidden knowledge of the
source model and exploit it to generate source avatar prototypes. To this end,
we propose a Contrastive Prototype Generation and Adaptation (CPGA) method.
CPGA consists of two stages: Prototype generation and Prototype adaptation.
Extensive experiments on three UDA benchmark datasets demonstrate the
superiority of CPGA. However, existing SF.UDA studies implicitly assume
balanced class distributions for both the source and target domains, which
hinders their real applications. To address this issue, we study a more
practical SF-UDA task, termed imbalance-agnostic SF-UDA, where the class
distributions of both the unseen source domain and unlabeled target domain are
unknown and could be arbitrarily skewed. This task is much more challenging
than vanilla SF-UDA due to the co-occurrence of covariate shifts and
unidentified class distribution shifts between the source and target domains.
To address this task, we extend CPGA and propose a new Target-aware Contrastive
Prototype Generation and Adaptation (T-CPGA) method. Specifically, for better
prototype adaptation in the imbalance-agnostic scenario, T-CPGA applies a new
pseudo label generation strategy to identify unknown target class distribution
and generate accurate pseudo labels, by utilizing the collective intelligence
of the source model and an additional contrastive language-image pre-trained
model. Meanwhile, we further devise a target label-distribution-aware
classifier to adapt the model to the unknown target class distribution. We
empirically show that T-CPGA significantly outperforms CPGA and other SF-UDA
methods in imbalance-agnostic SF-UDA.
