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Images seen during test time are often not from the same distribution asimages used for learning. This problem, known as domain shift, occurs whentraining classifiers from object-centric internet image databases and trying toapply them directly to scene understanding tasks. The consequence is oftensevere performance degradation and is one of the major barriers for theapplication of classifiers in real-world systems. In this paper, we show how tolearn transform-based domain adaptation classifiers in a scalable manner. Thekey idea is to exploit an implicit rank constraint, originated from amax-margin domain adaptation formulation, to make optimization tractable.Experiments show that the transformation between domains can be veryefficiently learned from data and easily applied to new categories. This beginsto bridge the gap between large-scale internet image collections and objectimages captured in everyday life environments.

Towards Adapting ImageNet to Reality: Scalable Domain Adaptation with Implicit Low-rank Transformations