One central challenge in source-free unsupervised domain adaptation (UDA) is the lack of an effective approach to evaluate the prediction results of the adapted network model in the target domain. To address this challenge, we propose to explore a new method called cross-inferential networks (CIN). Our main idea is that, when we adapt the network model to predict the sample labels from encoded features, we use these prediction results to construct new training samples with derived labels to learn a new examiner network that performs a different but compatible task in the target domain. Specifically, in this work, the base network model is performing image classification while the examiner network is tasked to perform relative ordering of triplets of samples whose training labels are carefully constructed from the prediction results of the base network model. Two similarity measures, cross-network correlation matrix similarity and attention consistency, are then developed to provide important guidance for the UDA process. Our experimental results on benchmark datasets demonstrate that our proposed CIN approach can significantly improve the performance of source-free UDA.

翻译：无源无监督域适应（UDA）的一个核心挑战在于缺乏有效方法来评估已适应网络模型在目标域中的预测结果。为解决该问题，我们提出了一种名为跨推理网络（CIN）的新方法。核心思想是：当网络模型从编码特征中预测样本标签时，我们将这些预测结果用于构建带有推导标签的新训练样本，进而训练一个在目标域中执行不同但兼容任务的评估网络。具体而言，本工作中基网络模型执行图像分类任务，而评估网络则负责对三元组样本进行相对排序，其训练标签由基网络模型的预测结果精心构建。随后，我们开发了两种相似性度量——跨网络相关性矩阵相似性与注意力一致性——为UDA过程提供关键指导。在基准数据集上的实验结果表明，我们提出的CIN方法能显著提升无源UDA的性能。