Abstract
Unsupervised domain adaptation (UDA) attempts to transfer knowledge learned from labeled source domain to unlabeled target domain. Its main challenge is distribution gap between two domains. Most of works focus on reducing domain shift by domain alignment methods. Although these methods can reduce the domain shift, the samples far from the class center of target domain are still easily misclassified. To solve the problem, we propose a new approach named Adversarial Domain Alignment Feature Similarity Enhancement Learning (AASE). It learns domain invariant features by adversarial game and correlation alignment to reduce the domain gap, and makes these features having better discrimination via joint central discrimination and feature similarity enhancement. AASE makes the learned features have better intra-class compactness and inter-class separability. AASE is evaluated on two datasets, and the results show that AASE has critical improvement in the performance of UDA.
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References
Chen, C., Chen, Z., Jiang, B., Jin, X.: Joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation. arXiv preprint arXiv:1808.09347 (2018)
Chu, W.-S., De la Torre, F., Cohn, J.F.: Selective transfer machine for personalized facial action unit detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3515–3522 (2013)
Donahue, J., et al.: Decaf: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)
DudÃk, M., Phillips, S.J., Schapire, R.E.: Correcting sample selection bias in maximum entropy density estimation. In: Advances in Neural Information Processing Systems, pp. 323–330 (2006)
Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)
Gong, B., Grauman, K., Sha, F.: Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation. In: International Conference on Machine Learning, pp. 222–230 (2013)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B.: Covariate shift by kernel mean matching. Dataset Shift Mach. Learn. 3(4), 131–160 (2009)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hubert Tsai, Y.-H., Yeh, Y.-R., Frank Wang, Y.C.. Learning cross-domain landmarks for heterogeneous domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5081–5090 (2016)
Hull, J.J.: A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 16(5), 550–554 (1994)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liu, M.-Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469–477 (2016)
Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. arXiv preprint arXiv:1502.02791 (2015)
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)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Unsupervised domain adaptation with residual transfer networks. In: Advances in Neural Information Processing Systems, pp. 136–144 (2016)
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Neural Information Processing Systems Workshop, pp. 1–9 (2011)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Rozantsev, A., Salzmann, M., Fua, P.: Beyond sharing weights for deep domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 801–814 (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
Sun, B., Feng, J., Saenko, K. Return of frustratingly easy domain adaptation. In: AAAI Conference on Artificial Intelligence, pp. 2058–2065 (2016)
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: Deep domain confusion: maximizing for domain invariance. arXiv preprint arXiv:1412.3474 (2014)
Wu, F., et al.: Intraspectrum discrimination and interspectrum correlation analysis deep network for multispectral face recognition. IEEE Trans. Cybern. 1–14 (2018)
Wu, F., et al.: Cross-project and within-project semisupervised software defect prediction: a unified approach. IEEE Trans. Reliab. 67(2), 581–597 (2018)
Wu, F., et al.: Semi-supervised multi-view individual and sharable feature learning for webpage classification. In: The World Wide Web Conference, pp. 3349–3355 (2019)
Zellinger, W., Grubinger, T., Lughofer, E., Natschläger, T., Saminger-Platz, S.: Central moment discrepancy (CMD) for domain-invariant representation learning. arXiv preprint arXiv:1702.08811 (2017)
Zhuo, J., Wang, S., Zhang, W., Huang, Q.: Deep unsupervised convolutional domain adaptation. In: ACM International Conference on Multimedia, pp. 261–269 (2017)
Acknowledgments
This work was supported by National Natural Science Foundation of China (No. 61702280), Natural Science Foundation of Jiangsu Province (No. BK20170900), National Postdoctoral Program for Innovative Talents (No. BX20180146), Scientific Research Starting Foundation for Introduced Talents in Nanjing University of Posts and Telecommunications (NUPTSF, No. NY217009), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX17_0794).
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Zhou, J., Wu, F., Sun, Y., Wu, S., Yang, M., Jing, XY. (2019). Adversarial Domain Alignment Feature Similarity Enhancement Learning for Unsupervised Domain Adaptation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_22
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