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I-Hong Jhuo, Dong Liu, D.T. Lee, Shih-Fu Chang. Robust Visual Domain Adaptation with Low-Rank Reconstruction. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

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Visual domain adaptation addresses the problem of adapting the sample distribution of the source domain to the target domain, where the recognition task is intended but the data distributions are different. In this paper, we present a low-rank reconstruction method to reduce the domain distribution disparity. Specifically, we transform the visual samples in the source domain into an intermediate representation such that each transformed source sample can be linearly reconstructed by the samples of the target domain. Unlike the existing work, our method captures the intrinsic relatedness of the source samples during the adaptation process while uncovering the noises and outliers in the source domain that cannot be adapted, making it more robust than previous methods. We formulate our problem as a constrained nuclear norm and $\ell_{2,1}$ norm minimization objective and then adopt the Augmented Lagrange Multiplier (ALM) method for the optimization. Extensive experiments on various visual adaptation tasks show that the proposed method consistently and significantly beats the state-of-the-art domain adaptation methods


Dong Liu
Shih-Fu Chang

BibTex Reference

   Author = {Jhuo, I-Hong and Liu, Dong and Lee, D.T. and Chang, Shih-Fu},
   Title = {Robust Visual Domain Adaptation with Low-Rank Reconstruction},
   BookTitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
   Year = {2012}

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