att_abstract={{With unconstrained data acquisition scenarios widely prevalent, the ability to handle changes in data distribution across training and testing data sets becomes important. One way to approach this problem is through domain adaptation, and
in this paper we primarily focus on the unsupervised scenario where the labeled source domain training data is accompanied by unlabeled target domain test data.We present a two-stage data-driven approach by generating intermediate data representations that could provide relevant information on the domain shift. Starting with a linear representation of domains in the form of generative subspaces of same dimensions for the source and target domains, we first utilize the underlying geometry of the space of these subspaces, the Grassmann manifold, to obtain a ‘shortest’ geodesic path between the two domains. We
then sample points along the geodesic to obtain intermediate cross-domain data representations, using which a discriminative classifier is learnt to estimate the labels of the target data. We subsequently incorporate non-linear representation of domains by considering an Reproducing Kernel Hilbert Space representation, and a low-dimensional manifold representation using Laplacian Eigenmaps, and also examine other domain adaptation settings such as (i) semi-supervised adaptation where the target domain is partially labeled, and (ii) multi-domain adaptation where there could be more than one domain in the source and/or target.
Finally, we supplement our adaptation technique with (i) fine-grained reference domains that are created by blending samples from the source and the target to provide some evidence on the actual domain shift, and (ii) a multi-class boosting analysis to obtain robustness to the choice of algorithm parameters. We evaluate our approach for object recognition problems and report competitive results on two widely used Office and Bing adaptation datasets.}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2014. {{, 2014-01-01}}
	author={Raghuraman Gopalan and Ruonan Li and Rama Chellappa},
	institution={{IEEE Transactions on Pattern Analysis and Machine Intelligence}},
	title={{Unsupervised Adaptation Across Domain Shifts By Generating Intermediate Data Representations}},