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Transfer learning with affine model transformation
Oct 18, 2022
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.
A General Class of Transfer Learning Regression without Implementation Cost
Jun 23, 2020
We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters and the choice of the density-ratio model, the proposed method can integrate three popular methods of TL: TL based on cross-domain similarity regularization, a probabilistic TL using the density-ratio estimation, and fine-tuning of pretrained neural networks. Moreover, the proposed method can benefit from its simple implementation without any additional cost; the model can be fully trained using off-the-shelf libraries for supervised learning in which the original output variable is simply transformed to a new output. We demonstrate its simplicity, generality, and applicability using various real data applications.