  Supervised transfer learning (TL) has received considerable attention because
of its potential to boost the predictive power of machine learning in cases
with limited data. In a conventional scenario, cross-domain differences are
modeled and estimated using a given set of source models and samples from a
target domain. For example, if there is a functional relationship between
source and target domains, only domain-specific factors are additionally
learned using target samples to shift the source models to the target. However,
the general methodology for modeling and estimating such cross-domain shifts
has been less studied. This study presents a TL framework that simultaneously
and separately estimates domain shifts and domain-specific factors using given
target samples. Assuming consistency and invertibility of the domain
transformation functions, we derive an optimal family of functions to represent
the cross-domain shift. The newly derived class of transformation functions
takes the same form as invertible neural networks using affine coupling layers,
which are widely used in generative deep learning. We show that the proposed
method encompasses a wide range of existing methods, including the most common
TL procedure based on feature extraction using neural networks. We also clarify
the theoretical properties of the proposed method, such as the convergence rate
of the generalization error, and demonstrate the practical benefits of
separately modeling and estimating domain-specific factors through several case
studies.

Machine Learning;Machine Learning