Due to privacy, storage, and other constraints, there is a growing need for
unsupervised domain adaptation techniques in machine learning that do not
require access to the data used to train a collection of source models.
Existing methods for such multi-source-free domain adaptation typically train a
target model using supervised techniques in conjunction with pseudo-labels for
the target data, which are produced by the available source models. However, we
show that assigning pseudo-labels to only a subset of the target data leads to
improved performance. In particular, we develop an information-theoretic bound
on the generalization error of the resulting target model that demonstrates an
inherent bias-variance trade-off controlled by the subset choice. Guided by
this analysis, we develop a method that partitions the target data into
pseudo-labeled and unlabeled subsets to balance the trade-off. In addition to
exploiting the pseudo-labeled subset, our algorithm further leverages the
information in the unlabeled subset via a traditional unsupervised domain
adaptation feature alignment procedure. Experiments on multiple benchmark
datasets demonstrate the superior performance of the proposed method.

该研究提出了一种利用信息论界限和特征对齐技术，将目标数据划分为伪标记子集和无标记子集以实现平衡的无监督域自适应方法，取得了比现有方法更好的性能表现。