Abstract
Many visual datasets are traditionally used to analyze the performance of different learning techniques. The evaluation is usually done within each dataset, therefore it is questionable if such results are a reliable indicator of true generalization ability. We propose here an algorithm to exploit the existing data resources when learning on a new multiclass problem. Our main idea is to identify an image representation that decomposes orthogonally into two subspaces: a part specific to each dataset, and a part generic to, and therefore shared between, all the considered source sets. This allows us to use the generic representation as un-biased reference knowledge for a novel classification task. By casting the method in the multi-view setting, we also make it possible to use different features for different databases. We call the algorithm MUST, Multitask Unaligned Shared knowledge Transfer. Through extensive experiments on five public datasets, we show that MUST consistently improves the cross-datasets generalization performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106, 59–70 (2007)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC 2007) Results (2007), http://www.pascal-network.org/challenges/VOC/
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between class attribute transfer. In: CVPR (2009)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR (2009)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: CVPR (2011)
Perronnin, F., Sánchez, J., Liu, Y.: Large-scale image categorization with explicit data embedding. In: CVPR (2010)
Stark, M.M., Riesenfeld, R.F.: Wordnet: An electronic lexical database. In: Eurographics Workshop on Rendering. MIT Press (1998)
Salakhutdinov, R., Torralba, A., Tenenbaum, J.: Learning to Share Visual Appearance for Multiclass Object Detection. In: CVPR (2011)
Blitzer, J., Crammer, K., Kulesza, A., Pereira, O., Wortman, J.: Learning bounds for domain adaptation. In: NIPS (2008)
Ben-david, S., Blitzer, J., Crammer, K., Sokolova, P.M.: Analysis of representations for domain adaptation. In: NIPS (2007)
Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: EMNLP (2006)
Daumé III, H.: Frustratingly easy domain adaptation. In: ACL (2007)
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)
Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: An unsupervised approach. In: ICCV (2011)
Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR (2012)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 1345–1359 (2010)
Jie, L., Tommasi, T., Caputo, B.: Multiclass transfer learning from unconstrained priors. In: ICCV (2011)
Evgeniou, T., Pontil, M.: Regularized multi–task learning. In: KDD (2004)
Kang, Z., Grauman, K., Sha, F.: Learning with whom to share in multi-task feature learning. In: ICML (2011)
Wang, X., Zhang, C., Zhang, Z.: Boosted multi-task learning for face verification with applications to web image and video search. In: CVPR (2009)
Vezhnevets, A., Buhmann, J.M.: Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning. In: CVPR (2010)
Quadrianto, N., Smola, A.J., Caetano, T.S., Vishwanathan, S.V.N., Petterson, J.: Multitask learning without label correspondences. In: NIPS (2010)
Parameswaran, S., Weinberger, K.: Large margin multi-task metric learning. In: NIPS (2010)
Leen, G.: Context assisted information extraction. PhD thesis, University of the West of Scotland (2008)
Jia, Y., Salzmann, M., Darrell, T.: Factorized latent spaces with structured sparsity. In: NIPS (2010)
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the Damage of Dataset Bias. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 158–171. Springer, Heidelberg (2012)
Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10, 207–244 (2009)
Goldberger, J., Roweis, S.T., Hinton, G.E., Salakhutdinov, R.: Neighbourhood components analysis. In: NIPS (2004)
Quadrianto, N., Lampert, C.H.: Learning multi-view neighborhood preserving projections. In: ICML (2011)
Kleiner, A., Rahimi, A., Jordan, M.I.: Random conic pursuit for semidefinite programming. In: NIPS (2010)
Lewis, A.S., Overton, M.L.: Nonsmooth optimization via quasi-newton methods. Math. Programming (to appear)
Microsoft Research Cambridge Object Recognition Image Database (2005), http://research.microsoft.com/en-us/downloads/
Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical Report MSc thesis, University of Toronto, USA (2007)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42, 145–175 (2001)
Gibbons, J.: Nonparametric Statistical Inference. Marcel Dekker, New York (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tommasi, T., Quadrianto, N., Caputo, B., Lampert, C.H. (2013). Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-37331-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37330-5
Online ISBN: 978-3-642-37331-2
eBook Packages: Computer ScienceComputer Science (R0)