Attracting and dispersing: A simple approach for source-free domain adaptation

S Yang, S Jui, J van de Weijer - Advances in Neural …, 2022 - proceedings.neurips.cc
We propose a simple but effective source-free domain adaptation (SFDA) method. Treating
SFDA as an unsupervised clustering problem and following the intuition that local neighbors …

Model adaptation: Unsupervised domain adaptation without source data

R Li, Q Jiao, W Cao, HS Wong… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
In this paper, we investigate a challenging unsupervised domain adaptation setting---
unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data …