Transfer learning in classification based on manifolc. models and its relation to tangent metric learning

The paper deals with realizations of transfer learning for classification, i. e. the adaptation of a classifier model to a changed data distribution. This change could be a data drift or a more complex transformation. We propose to model those data changes by manifolds describing continuous transformations of the data. This description can be seen as a generalization of function based transfer models considered so far. The manifold description...