Scalable Transform-based Domain Adaptation
نویسندگان
چکیده
In this paper, we show how to learn transform-based domain adaptation classifiers in a scalable manner. The key idea is to exploit an implicit rank constraint, originated from a max-margin domain adaptation formulation, to make optimization tractable. Experiments show that the transformation between domains can be very efficiently learned from data and easily applied to new categories. Source code can be found at: https://github. com/erodner/liblinear-mmdt.
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