Generalized Source-free Domain Adaptation
By Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, Shangling Jui. IEEE International Conference on Computer Vision, 2021
328 citations recorded by the source corpus.
Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains. Code is available in https://github.com/Albert0147/G-SFDA.
View Paper: https://www.semanticscholar.org/paper/18e2efe31e3ead5da291aba014f0c1decf197d21
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Luc Van Gool
- Dengxin Dai
- Trevor Darrell
- Dequan Wang
- Sayna Ebrahimi
- Jitendra Malik
- Kaiming He
- Sergey Levine
Citations in the corpus
- Contrastive Test-Time Adaptation (2022, background): “In this work we focus on the problem of test-time [50, 56] or source-free [23, 28, 58] domain adaptation where the source data is no longer available during adapting to unlabeled test data.”
- Continual Test-Time Domain Adaptation (2022, background): “Test-time adaptation is also referred to as source-free domain adaptation in some references [28,66].”
- A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts (2023, unknown): “data variations G-SFDA [129], APA [130] consistency model variations SFDA-UR [131], FMML [132] both variations AdaContrast [133], MAPS [134]”