Geodesic flow kernel for unsupervised domain adaptation

In real-world applications of visual recognition, many factors—such as pose, illumination, or image quality—can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic p...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Boqing Gong, Yuan Shi, Fei Sha, Kristen Grauman
• Keywords: Computer Vision, Cvpr 2012, Feature Representations, Target Domain, Target Domains