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.

该论文介绍了一种针对无源领域自适应的新方法，通过将目标领域的样本细分为多个未知类别来改善对目标专属样本的分离，并提出了一种名为 NL-InfoNCELoss 的新对比损失函数，可以增强模型对噪声伪标签的鲁棒性。实验结果表明，该方法在基准数据集上优于现有方法，并能学习新类别的潜在语义，为新类别的发现提供了可能。