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Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains.
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Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization. In: International Conference on Computer Vision (2019)
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Bai, H., Sun, R., Hong, L., Zhou, F., Ye, N., Ye, H.J., Chan, S.H.G., Li, Z.: Decaug: Out-of-distribution generalization via decomposed feature representation and semantic augmentation. AAAI Conference on Artificial Intelligence (2021)
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Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: International Conference on Computer Vision (2021)
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2022
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Shi, Y., Seely, J., Torr, P., N, S., Hannun, A., Usunier, N., Synnaeve, G.: Gradient matching for domain generalization. In: International Conference on Learning Representations (2022)
2022
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Singh, M., Gustafson, L., Adcock, A., de Freitas Reis, V., Gedik, B., Kosaraju, R.P., Mahajan, D., Girshick, R., Dollár, P., van der Maaten, L.: Revisiting weakly supervised pre-training of visual perception models. In: Computer Vision and Pattern Recognition (2022)
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