Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation
  with Target-private Class Segregation
Mattia Litrico Davide Talon Sebastiano Battiato Alessio Del Bue Mario Valerio Giuffrida Pietro Morerio
Standard Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from
a labeled source domain to an unlabeled target but usually requires
simultaneous access to both source and target data. Moreover, UDA approaches
commonly assume that source and target domains share the same labels space.
Yet, these two assumptions are hardly satisfied in real-world scenarios. This
paper considers the more challenging Source-Free Open-set Domain Adaptation
(SF-OSDA) setting, where both assumptions are dropped. We propose a novel
approach for SF-OSDA that exploits the granularity of target-private categories
by segregating their samples into multiple unknown classes. Starting from an
initial clustering-based assignment, our method progressively improves the
segregation of target-private samples by refining their pseudo-labels with the
guide of an uncertainty-based sample selection module. Additionally, we propose
a novel contrastive loss, named NL-InfoNCELoss, that, integrating negative
learning into self-supervised contrastive learning, enhances the model
robustness to noisy pseudo-labels. Extensive experiments on benchmark datasets
demonstrate the superiority of the proposed method over existing approaches,
establishing new state-of-the-art performance. Notably, additional analyses
show that our method is able to learn the underlying semantics of novel
classes, opening the possibility to perform novel class discovery.