Domain Adaptation with a Domain Specific Class Means Classifier
Conference paper
First Online:
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.
Keywords
Domain adaptation Self-adative metric learning NCM Download
to read the full conference paper text
References
- 1.Baktashmotlagh, M., Harandi, M.T., Lovell, B.C., Salzmann, M.: Unsupervised domain adaptation by domain invariant projection. In: ICCV (2013)Google Scholar
- 2.Beijbom, O.: Domain adaptations for computer vision applications. University of California, San Diego (June (2012)Google Scholar
- 3.Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: SLCV (ECCV Workshop) (2004)Google Scholar
- 4.Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: ICML (2007)Google Scholar
- 5.Duan, L., Tsang, I.W., Xu, D., Maybank, S.J.: Domain transfer SVM for video concept detection. In: CVPR (2009)Google Scholar
- 6.Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: Unsupervised visual domain adaptation using subspace alignment. In: ICCV (2013)Google Scholar
- 7.Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR (2012)Google Scholar
- 8.Gong, B., Grauman, K., Sha, F.: Reshaping visual datasets for domain adaptation. In: NIPS (2013)Google Scholar
- 9.Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: ICCV (2011)Google Scholar
- 10.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) CrossRefGoogle Scholar
- 11.Huang, J., Smola, A., A., Borgwardt, K., Schoelkopf, B.: Correcting sample selection bias by unlabeled data. In: NIPS (2007)Google Scholar
- 12.Jiang, J.: A literature survey on domain adaptation of statistical classifiers. Tech. rep. (2008)Google Scholar
- 13.Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: Domain adaptation using asymmetric kernel transforms. In: CVPR (2011)Google Scholar
- 14.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)CrossRefGoogle Scholar
- 15.Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
- 16.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) CrossRefGoogle Scholar
- 17.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) CrossRefGoogle Scholar
- 18.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) CrossRefGoogle Scholar
- 19.Tommasi, T., Caputo, B.: Frustratingly easy nbnn domain adaptation. In: ICCV (2013)Google Scholar
- 20.Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. JMLR 10, 207–244 (2009)zbMATHGoogle Scholar
- 21.Zha, Z.J., Mei, T., Wang, M., Wang, Z., Hua, X.S.: Robust distance metric learning with auxiliary knowledge. In: IJCAI (2009)Google Scholar
Copyright information
© Springer International Publishing Switzerland 2015