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Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data used to train a collection of source models.
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Algorithms and theory for multiple-source adaptation
Hoffman, J., Mohri, M., and Zhang, N · 2018
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Transductive semi-supervised deep learning using min-max features
Shi, W., Gong, Y., Ding, C., Tao, Z. M., and Zheng, N · 2018
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Adversarial multiple source domain adaptation
Zhao, H., Zhang, S., Wu, G., Moura, J. M., Costeira, J. P., and Gordon, G. J · 2018
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Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., and Wang, B · 2019
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Multi-source distilling domain adaptation
Zhao, S., Wang, G., Zhang, S., Gu, Y., Li, Y., Song, Z., Xu, P., Hu, R., Chai, H., and Keutzer, K · 2020
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Unsupervised multi-source domain adaptation without access to source data
Ahmed, S. M., Raychaudhuri, D. S., Paul, S., Oymak, S., and Roy-Chowdhury, A. K · 2021
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Confident anchor-induced multi-source free domain adaptation
Dong, J., Fang, Z., Liu, A., Sun, G., and Liu, T · 2021
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Domain impression: A source data free domain adaptation method
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Privacy preservation in federated learning: An insightful survey from the GDPR perspective
Truong, N., Sun, K., Wang, S., Guitton, F., and Guo, Y · 2021
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Adaptive adversarial network for source-free domain adaptation
Xia, H., Zhao, H., and Ding, Z · 2021
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Prototypical pseudo label denoising and target structure learning for domain adaptive semantic segmentation
Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., and Wen, F · 2021
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Balancing discriminability and transferability for source-free domain adaptation
Kundu, J. N., Kulkarni, A. R., Bhambri, S., Mehta, D., Kulkarni, S. A., Jampani, V., and Radhakrishnan, V. B · 2022
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