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We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains.
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Salimans, T., Kingma, D.P.: Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In: NIPS (2016)
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Li, D., Yang, Y., Song, Y.Z., Hospedales, T.M.: Deeper, broader and artier domain generalization. In: ICCV (2017)
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Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.: Unified deep supervised domain adaptation and generalization. In: ICCV (2017)
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Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis. In: CVPR (2017)
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Venkateswara, H., Eusebio, J., Chakraborty, S., Panchanathan, S.: Deep Hashing Network for Unsupervised Domain Adaptation. In: CVPR (2017)
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Balaji, Y., Sankaranarayanan, S., Chellappa, R.: Metareg: Towards domain generalization using meta-regularization. In: NeurIPS (2018)
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DaInnocente, A., Caputo, B.: Domain generalization with domain-specific aggregation modules. In: GCPR (2018)
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Guo, J., Shah, D., Barzilay, R.: Multi-source domain adaptation with mixture of experts. In: EMNLP (2018)
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Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: Meta-learning for domain generalization. In: AAAI (2018)
2018
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Li, Y., Gong, M., Tian, X., Liu, T., Tao, D.: Domain generalization via conditional invariant representations. In: AAAI (2018)
2018
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Wu, Y., He, K.: Group Normalization. In: ECCV (2018)
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Xu, R., Chen, Z., Zuo, W., Yan, J., Lin, L.: Deep cocktail network: Multi-source unsupervised domain adaptation with category shift. In: CVPR (2018)
2018
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2018
Later among the works it cites.
Carlucci, F.M., D’Innocente, A., Bucci, S., Caputo, B., Tommasi, T.: Domain generalization by solving jigsaw puzzles. In: CVPR (2019)
2019
Closest in time.
Chang, W.G., You, T., Seo, S., Kwak, S., Han, B.: Domain specific batch normalization for unsupervised domain adaptation. In: CVPR (2019)
2019
Closest in time.
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Mancini, M., Bulò, S.R., Caputo, B., Ricci, E.: Best sources forward: domain generalization through source-specific nets. In: ICIP (2018)
2018
Cited alongside, same era.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: ICLR (2018)
2018
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Nam, H., Kim, H.E.: Batch-instance normalization for adaptively style-invariant neural networks. In: NeurIPS (2018)
2018
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Pan, X., Luo, P., Shi, J., Tang, X.: Two at once: Enhancing learning and generalization capacities via ibn-net. In: ECCV (2018)
2018
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Shu, Y., Cao, Z., Long, M., Wang, J.: Transferable curriculum for weakly-supervised domain adaptation. In: AAAI (2018)
2018
Cited alongside, same era.
Dou, Q., de Castro, D.C., Kamnitsas, K., Glocker, B.: Domain generalization via model-agnostic learning of semantic features. In: NeruIPS (2019)
2019
Closest in time.
Li, D., Zhang, J., Yang, Y., Liu, C., Song, Y.Z., Hospedales, T.M.: Episodic training for domain generalization (2019)
2019
Closest in time.
Luo, P., Ren, J., Peng, Z.: Differentiable learning-to-normalize via switchable normalization. In: ICLR (2019)
2019
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Shao, W., Meng, T., Li, J., Zhang, R., Li, Y., Wang, X., Luo, P.: Ssn: Learning sparse switchable normalization via sparsestmax. In: CVPR (2019)
2019
Closest in time.
Matsuura, T., Harada, T.: Domain generalization using a mixture of multiple latent domains. In: AAAI (2020)
2020
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