We investigate a practical domain adaptation task, called source-free domain
adaptation (SFUDA), where the source-pretrained model is adapted to the target
domain without access to the source data. Existing techniques mainly leverage
self-supervised pseudo labeling to achieve class-wise global alignment [1] or
rely on local structure extraction that encourages feature consistency among
neighborhoods [2]. While impressive progress has been made, both lines of
methods have their own drawbacks - the "global" approach is sensitive to noisy
labels while the "local" counterpart suffers from source bias. In this paper,
we present Divide and Contrast (DaC), a new paradigm for SFUDA that strives to
connect the good ends of both worlds while bypassing their limitations. Based
on the prediction confidence of the source model, DaC divides the target data
into source-like and target-specific samples, where either group of samples is
treated with tailored goals under an adaptive contrastive learning framework.
Specifically, the source-like samples are utilized for learning global class
clustering thanks to their relatively clean labels. The more noisy
target-specific data are harnessed at the instance level for learning the
intrinsic local structures. We further align the source-like domain with the
target-specific samples using a memory bank-based Maximum Mean Discrepancy
(MMD) loss to reduce the distribution mismatch. Extensive experiments on VisDA,
Office-Home, and the more challenging DomainNet have verified the superior
performance of DaC over current state-of-the-art approaches. The code is
available at this https URL.

本文探讨了一种实际的领域自适应任务，称为无源域自适应（SFUDA），在此任务中，源预训练模型在没有访问源数据的情况下适应于目标域。我们介绍了一种新的 SFUDA 范例 Divide and Contrast（DaC），使用自适应对比学习框架，通过预测的置信度将目标数据分为类似源域和特定于目标域的样本，并针对每个组别进行调整目标，以在全局和局部层面上提高性能