Abstract
This paper deals with the problem of unsupervised domain adaptation that aims to learn a classifier with a slight target risk while labeled samples are only available in the source domain. The proposed approach, called DA-SSL (Domain Adaptation meets Semi-Supervised Learning) attempts to find a joint subspace of the source and target domains using Linear Discriminant Analysis, such that the projections of the data into this latent subspace can be both domain invariant and discriminative. This aim, however, can be rather difficult to accomplish because of the missing labeled data in the target domain. To defeat this challenge, we use an incremental semi-supervised approach based on optimal transport theory, that conducts selective pseudo-labeling for unlabeled target instances. The selected pseudo-labeled target data are then combined with the source data to incrementally learn a robust classifier in a self-training fashion after the subspace alignment. Experiments show the competitiveness of the proposed approach over contemporary state-of-the-art methods on two benchmark domain adaptation datasets. We make our code publicly available (Code is available at: https://github.com/MouradElHamri/DA-SSL).
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References
Benabdeslem, K., Bennani, Y.: Dendrogram-based SVM for multi-class classification. J. Comput. Inf. Techno 14(4), 283–289 (2006)
Caputo, B., et al.: ImageCLEF 2014: overview and analysis of the results. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 192–211. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11382-1_18
Cuturi, M.: Sinkhorn distances: Lightspeed computation of optimal transport. In: Advances in Neural Information Processing Systems, pp. 2292–2300 (2013)
El Hamri, M., Bennani, Y., Falih, I.: Label propagation through optimal transport. In: 2021 International Joint Conference on Neural Networks (2021)
El Hamri, M., Bennani, Y., Falih, I.: Inductive semi-supervised learning through optimal transport. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. CCIS, vol. 1516, pp. 668–675. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92307-5_78
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066–2073 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kantorovich, L.V.: On the translocation of masses. Dokl. Akad. Nauk. USSR (NS) 37, 199–201 (1942)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Monge, G.: Mémoire sur la théorie des déblais et des remblais. Histoire de l’Académie Royale des Sciences de Paris (1781)
Pei, Z., Cao, Z., Long, M., Wang, J.: Multi-adversarial domain adaptation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Peyré, G., Cuturi, M., et al.: Computational optimal transport: with applications to data science. Found. Trends® Mach. Learn. (2019)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_16
Wang, J., Feng, W., Chen, Y., Yu, H., Huang, M., Yu, P.S.: Visual domain adaptation with manifold embedded distribution alignment. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 402–410 (2018)
Wei, C., Shen, K., Chen, Y., Ma, T.: Theoretical analysis of self-training with deep networks on unlabeled data. arXiv preprint arXiv:2010.03622 (2020)
Zhang, W., Ouyang, W., Li, W., Xu, D.: Collaborative and adversarial network for unsupervised domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3801–3809 (2018)
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El Hamri, M., Bennani, Y., Falih, I. (2022). When Domain Adaptation Meets Semi-supervised Learning Through Optimal Transport. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-08333-4_5
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