  Unsupervised domain adaptation (UDA) aims to learn models for a target domain
of unlabeled data by transferring knowledge from a labeled source domain. In
the traditional UDA setting, labeled source data are assumed to be available
for adaptation. Due to increasing concerns for data privacy, source-free UDA is
highly appreciated as a new UDA setting, where only a trained source model is
assumed to be available, while labeled source data remain private. However,
trained source models may also be unavailable in practice since source models
may have commercial values and exposing source models brings risks to the
source domain, e.g., problems of model misuse and white-box attacks. In this
work, we study a subtly different setting, named Black-Box Unsupervised Domain
Adaptation (B$^2$UDA), where only the application programming interface of
source model is accessible to the target domain; in other words, the source
model itself is kept as a black-box one. To tackle B$^2$UDA, we propose a
simple yet effective method, termed Iterative Learning with Noisy Labels
(IterLNL). With black-box models as tools of noisy labeling, IterLNL conducts
noisy labeling and learning with noisy labels (LNL), iteratively. To facilitate
the implementation of LNL in B$^2$UDA, we estimate the noise rate from model
predictions of unlabeled target data and propose category-wise sampling to
tackle the unbalanced label noise among categories. Experiments on benchmark
datasets show the efficacy of IterLNL. Given neither source data nor source
models, IterLNL performs comparably with traditional UDA methods that make full
use of labeled source data.
