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Existing domain adaptation (DA) methods often involve pre-training on the source domain and fine-tuning on the target domain.
“Domain-adversarial training of neural networks,”
Y. Ganin et al., · 2016
Earlier work this paper cites.
“Revisiting batch normalization for practical domain adaptation,”
Y. Li et al., · 2016
Earlier work this paper cites.
“Fcns in the wild: Pixel-level adversarial and constraint-based adaptation,”
J. Hoffman et al., · 2016
Earlier work this paper cites.
“Preparing a collection of radiology examinations for distribution and retrieval,”
D. Demner-Fushman et al., · 2016
Earlier work this paper cites.
“Multi-scale patch and multi-modality atlases for whole heart segmentation of mri,”
Xiahai Zhuang and Juan Shen, · 2016
Earlier work this paper cites.
“Unpaired image-to-image translation using cycle-consistent adversarial networks,”
J-Y Zhu et al., · 2017
Earlier work this paper cites.
“Domain adaptive faster R-CNN for object detection in the wild,”
Y. Chen et al., · 2018
Earlier work this paper cites.
“Domain transfer through deep activation matching,”
H. Huang et al., · 2018
Earlier work this paper cites.
“Domain adaptation for MRI organ segmentation using reverse classification accuracy,”
V. V. Valindria et al., · 2018
Earlier work this paper cites.
“An open, multi-vendor, multi-field-strength brain mr dataset and analysis of publicly available skull stripping methods agreement,”
R. Souza et al., · 2018
Earlier work this paper cites.
“Unsupervised domain adaptation for semantic segmentation via class-balanced self-training,”
Y. Zou et al., · 2018
Cited alongside, same era.
“Bidirectional learning for domain adaptation of semantic segmentation,”
Y. Li et al., · 2019
Cited alongside, same era.
“Supervised domain adaptation for automatic sub-cortical brain structure segmentation with minimal user interaction,”
K. Kushibar et al., · 2019
Cited alongside, same era.
“Domain-specific batch normalization for unsupervised domain adaptation,”
W-G Chang et al., · 2019
Cited alongside, same era.
“Parameter-efficient transfer learning for NLP,”
N. Houlsby et al., · 2019
Cited alongside, same era.
“Advancing medical imaging informatics by deep learning-based domain adaptation,”
A. Choudhary et al., · 2020
“Prefix-tuning: Optimizing continuous prompts for generation,”
Xiang Lisa Li and Percy Liang, · 2021
Later among the works it cites.
“The power of scale for parameter-efficient prompt tuning,”
B. Lester et al., · 2021
Later among the works it cites.
“Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes,”
S. Choi et al., · 2022
Later among the works it cites.
“Continual test-time domain adaptation,”
Q. Wang et al., · 2022
Later among the works it cites.
“Unsupervised domain adaptation for medical image segmentation via self-training of early features,”
Rasha Sheikh and Thomas Schultz, · 2022
Later among the works it cites.
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Cited alongside, same era.
“First U-Net layers contain more domain specific information than the last ones,”
B. Shirokikh et al., · 2020
Cited alongside, same era.
“Lora: Low-rank adaptation of large language models,”
E. Hu et al., · 2021
Cited alongside, same era.
“Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models,”
E.B. Zaken et al., · 2021
Cited alongside, same era.
“Ttt++: When does self-supervised test-time training fail or thrive?,”
Y. Liu et al., · 2021
Cited alongside, same era.
“The norm must go on: Dynamic unsupervised domain adaptation by normalization,”
M.J. Mirza et al., · 2022
Later among the works it cites.
“Med-tuning: Exploring parameter-efficient transfer learning for medical volumetric segmentation,”
W. Wang et al., · 2023
Later among the works it cites.
“Medical sam adapter: Adapting segment anything model for medical image segmentation,”
J. Wu et al., · 2023
Later among the works it cites.
“Parameter-efficient fine-tuning for medical image analysis: The missed opportunity,”
R. Dutt et al., · 2023
Later among the works it cites.