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Found 4 papers, 1 papers with code
Unknown Sample Discovery for Source Free Open Set Domain Adaptation
Source-free OSDA (SF-OSDA) techniques eliminate the need to access source domain samples, but current SF-OSDA methods utilize only the known classes in the target domain for adaptation, and require access to the entire target domain even during inference after adaptation, to make the distinction between known and unknown samples.
Curriculum Guided Domain Adaptation in the Dark
Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters.
Continual Domain Adaptation on Aerial Images under Gradually Degrading Weather
We synthesize two such gradually worsening weather conditions on real images from two existing aerial imagery datasets, generating a total of four benchmark datasets.
ConDA: Continual Unsupervised Domain Adaptation
Our results on three popular DA datasets demonstrate that our method outperforms many existing state-of-the-art DA methods with access to the entire target domain during adaptation.
