Abstract
We consider the problem of learning a classifier when we dispose little training data from the target domain but abundant training data from several source domains. We make two contributions to the domain adaptation problem. First we extend the Nearest Class Mean (NCM) classifier by introducing for each class domain-dependent mean parameters as well as domain-specific weights. Second, we propose a generic adaptive semi-supervised metric learning technique that iteratively curates the training set by adding unlabeled samples with high prediction confidence and by removing labeled samples for which the prediction confidence is low. These two complementary techniques are evaluated on two public benchmarks: the ImageClef Domain Adaptation Challenge and the Office-CalTech datasets. Both contributions are shown to yield improvements and to be complementary to each other.
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Baktashmotlagh, M., Harandi, M.T., Lovell, B.C., Salzmann, M.: Unsupervised domain adaptation by domain invariant projection. In: ICCV (2013)
Beijbom, O.: Domain adaptations for computer vision applications. University of California, San Diego (June (2012)
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: SLCV (ECCV Workshop) (2004)
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML (2007)
Duan, L., Tsang, I.W., Xu, D., Maybank, S.J.: Domain transfer SVM for video concept detection. In: CVPR (2009)
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: ICCV (2013)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR (2012)
Gong, B., Grauman, K., Sha, F.: Reshaping visual datasets for domain adaptation. In: NIPS (2013)
Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: ICCV (2011)
Hoffman, J., Kulis, B., Darrell, T., Saenko, K.: Discovering Latent Domains for Multisource Domain Adaptation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 702–715. Springer, Heidelberg (2012)
Huang, J., Smola, A., A., Borgwardt, K., Schoelkopf, B.: Correcting sample selection bias by unlabeled data. In: NIPS (2007)
Jiang, J.: A literature survey on domain adaptation of statistical classifiers. Tech. rep. (2008)
Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In: CVPR (2011)
Mensink, T., Verbeek, J., Perronnin, F., Csurka, G.: Distance-based image classification: Generalizing to new classes at near zero cost. PAMI 35(11), 2624–2637 (2013)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)
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, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)
Saha, A., Rai, P., Daumé III, H., Venkatasubramanian, S., DuVall, S.L.: Active Supervised Domain Adaptation. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 97–112. Springer, Heidelberg (2011)
Tommasi, T., Caputo, B.: Frustratingly easy nbnn domain adaptation. In: ICCV (2013)
Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. JMLR 10, 207–244 (2009)
Zha, Z.J., Mei, T., Wang, M., Wang, Z., Hua, X.S.: Robust distance metric learning with auxiliary knowledge. In: IJCAI (2009)
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Csurka, G., Chidlovskii, B., Perronnin, F. (2015). Domain Adaptation with a Domain Specific Class Means Classifier. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8927. Springer, Cham. https://doi.org/10.1007/978-3-319-16199-0_3
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DOI: https://doi.org/10.1007/978-3-319-16199-0_3
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