Feature-Level Domain Adaptation

no code implementations • 15 Dec 2015 • Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten

Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain.

Domain Adaptation