Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task
where a model needs to be adapted to a new domain without access to target
domain labels or source domain data. The primary difficulty in this task is
that the model's predictions may be inaccurate, and using these inaccurate
predictions for model adaptation can lead to misleading results. To address
this issue, this paper proposes a novel approach that considers multiple
prediction hypotheses for each sample and investigates the rationale behind
each hypothesis. By consolidating these hypothesis rationales, we identify the
most likely correct hypotheses, which we then use as a pseudo-labeled set to
support a semi-supervised learning procedure for model adaptation. To achieve
the optimal performance, we propose a three-step adaptation process: model
pre-adaptation, hypothesis consolidation, and semi-supervised learning.
Extensive experimental results demonstrate that our approach achieves
state-of-the-art performance in the SFUDA task and can be easily integrated
into existing approaches to improve their performance. The codes are available
at https://github.com/GANPerf/HCPR.

无源无监督领域自适应（SFUDA）是一个具有挑战性的任务，模型需要在没有目标领域标签或源领域数据的情况下适应新领域。本文提出了一种新的方法，通过考虑每个样本的多个预测假设并研究每个假设的合理性来解决该问题。通过整合这些假设的理由，我们可以识别最有可能的正确假设，并将其作为伪标签集来支持半监督学习过程进行模型自适应。我们提出了一个三步自适应过程：模型预自适应、假设整合和半监督学习，以实现最佳性能。广泛的实验结果表明我们的方法在 SFUDA 任务中达到了最先进的性能，并可以轻松集成到现有方法中以改善其性能。