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.

提出了一种基于交叉推理网络的新方法，用于解决无源自适应领域中预测结果评估的问题，结果表明这种方法可以显著提高源自由无监督领域适应的性能。