  Source-free domain adaptation (SFDA) aims to adapt a well-trained source
model to an unlabelled target domain without accessing the source dataset,
making it applicable in a variety of real-world scenarios. Existing SFDA
methods ONLY assess their adapted models on the target training set, neglecting
the data from unseen but identically distributed testing sets. This oversight
leads to overfitting issues and constrains the model's generalization ability.
In this paper, we propose a consistency regularization framework to develop a
more generalizable SFDA method, which simultaneously boosts model performance
on both target training and testing datasets. Our method leverages soft
pseudo-labels generated from weakly augmented images to supervise strongly
augmented images, facilitating the model training process and enhancing the
generalization ability of the adapted model. To leverage more potentially
useful supervision, we present a sampling-based pseudo-label selection
strategy, taking samples with severer domain shift into consideration.
Moreover, global-oriented calibration methods are introduced to exploit global
class distribution and feature cluster information, further improving the
adaptation process. Extensive experiments demonstrate our method achieves
state-of-the-art performance on several SFDA benchmarks, and exhibits
robustness on unseen testing datasets.
