GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
<|reference_start|>GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning: Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies.<|reference_end|>
@article{qu2024glc++:,
    title={GLC++: Source-Free Universal Domain Adaptation through Global-Local
  Clustering and Contrastive Affinity Learning},
    author={Sanqing Qu, Tianpei Zou, Florian R"ohrbein, Cewu Lu, Guang Chen,
  Dacheng Tao, Changjun Jiang},
    journal={arXiv preprint arXiv:2403.14410},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14410},
    primaryClass={cs.CV cs.AI cs.LG}
}