3 papers
cs.CV2026
One-shot federated unsupervised domain adaptation with Smoothed Knowledge Distillation and teacher refinement
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated unsupervised domain adaptation (FUDA) addresses the challenge of adapting models to an unlabeled target domain using decentralized source domains while preserving data pr…
cs.CV2025
One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning. However, it faces significant challenges when dealing with domain shifts, especially…
cs.CV2024
Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment
Ali Abedi, Q. M. Jonathan Wu, Ning Zhang +1
Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data. Despite recent advance…