Source-free domain adaptation (SFDA) aims to adapt a source model trained on a fully-labeled source domain to an unlabeled target domain. Large-data pre-trained networks are used to initialize source models during source training, and subsequently discarded. However, source training can cause the model to overfit to source data distribution and lose applicable target domain knowledge. We propose to integrate the pre-trained network into the target adaptation process as it has diversified features important for generalization and provides an alternate view of features and classification decisions different from the source model. We propose to distil useful target domain information through a co-learning strategy to improve target pseudolabel quality for finetuning the source model. Evaluation on 4 benchmark datasets show that our proposed strategy improves adaptation performance and can be successfully integrated with existing SFDA methods. Leveraging modern pre-trained networks that have stronger representation learning ability in the co-learning strategy further boosts performance.

翻译：无源域适应旨在将在完全标记的源域上训练的源模型适配到未标记的目标域。在源训练过程中，大规模预训练网络被用于初始化源模型，随后被丢弃。然而，源训练可能导致模型对源数据分布过拟合，从而丢失适用的目标域知识。我们提出将预训练网络整合到目标适应过程中，因为它具有对泛化至关重要的多样化特征，并能提供与源模型不同的特征和分类决策视角。我们通过协同学习策略提炼有用的目标域信息，以提高用于微调源模型的目标伪标签质量。在四个基准数据集上的评估表明，我们提出的策略可提升适应性能，并能与现有无源域适应方法成功集成。在协同学习策略中利用具有更强表示学习能力的现代预训练网络还可进一步提升性能。