Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation
  of Prediction Rationale
Yangyang Shu Xiaofeng Cao Qi Chen Bowen Zhang Ziqin Zhou Anton van den Hengel Lingqiao Liu
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 \url{https://github.com/GANPerf/HCPR}.