no code implementations • 5 Dec 2023 • Chowdhury Sadman Jahan, Andreas Savakis
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.
no code implementations • 2 Aug 2023 • Chowdhury Sadman Jahan, Andreas Savakis
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.
1 code implementation • 2 Aug 2023 • Chowdhury Sadman Jahan, Andreas Savakis
We synthesize two such gradually worsening weather conditions on real images from two existing aerial imagery datasets, generating a total of four benchmark datasets.
no code implementations • 19 Mar 2021 • Abu Md Niamul Taufique, Chowdhury Sadman Jahan, Andreas Savakis
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.