Continual source-free domain adaptation is a new and practical task in the field of domain adaptation, which seeks to preserve the performance of a model across all domains encountered during the adaptation process while also protecting the privacy of private data, as illustrated in the following ...
Domain Adaptation (DA):此时,我们有源数据 + 源标签 + 目标数据,希望模型做到在没看过的⽬标数据上性能好,目标数据的标签限制到很少或者为零。 Domain Generalization (DG):此时,我们有源数据 + 源标签,希望模型做到在⽬标数据上性能好。 Target Data Adaptation Source-Free Domain Adaptation (SFDA):在上面...
In our work, we introduce a new, data-constrained DA paradigm where unlabeled target samples are received in batches and adaptation is performed continually. We propose a novel source-free method for continual unsupervised domain adaptation that utilizes a buffer for selective replay of previously ...
2.2. Test-time Adaptation Test-time adaptation is also referred to as source-free do- main adaptation in some references [28,66]. Unlike domain adaptation which requires access to both source and target data for adaptation, test-time adaptation methods do not re- quire any data from the ...
Unsup. Domain Adaptation (UDA) ✗ ✗ ✓ Domain-Incremental Learning (DIL) ✓ ✓ ✗ Source-free UDA/UMA ✓ ✗ ✓ Incremental UDA ✗ ✓ ✓ Continual UDA ✓ ✓ ✓ In contrast to the above, incremental UDA aims at adapting models to multiple target domains in an uns...
CLMS: Bridging Domain Gaps in Medical Imaging Segmentation with Source-Free Continual Learning for Robust Knowledge Transfer and Adaptation [Medical Image Analysis 2024] [paper] AWF: Adaptive Weight Fusion for Enhanced Class Incremental Semantic Segmentation [ArXiv 2024] [paper] CIT: Rethinking Class...
Continual Learning (CL) is a novel AI paradigm in which tasks and data are made available over time; thus, the trained model is computed on the basis of a
Moment matching for multi-source domain adaptation. In Proceedings of the IEEE/CVF In- ternational Conference on Computer Vision (ICCV), pages 1406–1415, 2019. [36] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. Improving languag...
Paper tables with annotated results for Color Prompting for Data-Free Continual Unsupervised Domain Adaptive Person Re-Identification
Wang, Moment matching for multi-source domain adaptation, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1406–1415. Google Scholar [84] P.O. Pinheiro, Unsupervised domain adaptation with similarity learning, in: Proceedings of the IEEE Conference on ...