Domain Adaptation with Nonparametric Projections

2019-08-22
Domain adaptation algorithms focus on a setting where the training and test data are sampled from related but different distributions. Various domain adaptation methods aim to align the source and target domains in a new common domain by learning a transformation or projection. In this work, we learn a nonlinear and nonparametric projection of the source and target domains into a common domain along with a linear classifier in the new domain. Experiments on image data sets show that the proposed nonlinear approach outperforms baseline domain adaptation methods based on linear transformations.

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Citation Formats
E. Vural, “Domain Adaptation with Nonparametric Projections,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40667.