Abstract
A prevailing problem in many machine learning tasks is that the training and test data have different distribution (non i.i.d). Previous methods to solve this problem are called Transfer Learning (TL) or Domain Adaptation (DA), which belong to one stage models. In this paper, we propose a new, simple but effective paradigm, Guided Learning (GL), for multi-stage progressive training. This new paradigm is motivated by the “tutor guides student” learning mode in human world. Further, under the framework of GL, a Guided Subspace Learning (GSL) method is proposed for domain disparity reduction, which aims to learn an optimal, invariant and discriminative subspace through the guided learning strategy. Extensive experiments on various databases show that our method outperforms many state-of-the-art TL/DA methods.
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Jian Feng Cai, C., Emmanuel, J.S., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2008)
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: IEEE ICCV, pp. 2960–2967 (2014)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: IEEE CVPR, pp. 2066–2073 (2012)
Hong Jhuo, I, Liu, D., Lee, D.T., Chang, S.F.: Robust visual domain adaptation with low-rank reconstruction. In: CVPR, pp. 2168–2175 (2012)
Kan, M., Junting, W., Shan, S., Chen, X.: Domain adaptation for face recognition: targetize source domain bridged by common subspace. IJCV 109(1–2), 94–109 (2014)
Lei, Z., Zhang, D.: Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Trans. IP 25(10), 4959–4973 (2016)
Long, M., Wang, J., Ding, G., Sun, J., Yu P.S.: Transfer feature learning with joint distribution adaptation. In: IEEE ICCV, pp. 2200–2207 (2014)
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer joint matching for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1410–1417 (2014)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Shao, M., Kit, D., Yun, F.: Generalized transfer subspace learning through low-rank constraint. IJCV 109(1–2), 74–93 (2014)
Si, S., Tao, D., Geng, B.: Bregman divergence-based regularization for transfer subspace learning. IEEE Trans. Knowl. Data Eng. 22(7), 929–942 (2010)
Sun, B., Feng, J., Saenko, K.: Correlation alignment for unsupervised domain adaptation. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 153–171. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58347-1_8
Sun, B., Saenko, K.: Subspace distribution alignment for unsupervised domain adaptation. In: BMVC, pp. 24.1–24.10 (2015)
Wright, J., Ganesh, A., Rao, S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices. J. ACM 87(4), 20:3–20:56 (2009)
Xu, Y., Fang, X., Wu, J., Li, X., Zhang, D.: Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans. IP 25(2), 850–863 (2016)
Yang, J., Yan, R., Hauptmann, A.G.: Cross-domain video concept detection using adaptive SVMS. In: ACM International Conference on Multimedia, pp. 188–197 (2007)
Zhang, L., Zuo, W., Zhang, D.: LSDT: latent sparse domain transfer learning for visual adaptation. IEEE Trans. IP 25(3), 1177–1191 (2016)
Acknowledgements
This work was supported by the National Science Fund of China under Grants (61771079).
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Fu, J., Zhang, L., Zhang, B., Jia, W. (2018). Guided Learning: A New Paradigm for Multi-task Classification. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_26
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DOI: https://doi.org/10.1007/978-3-319-97909-0_26
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