A General Class of Transfer Learning Regression without Implementation Cost

• URL: http://arxiv.org/abs/2006.13228v2
• Date: Thu, 17 Dec 2020 04:08:42 GMT
• Title: A General Class of Transfer Learning Regression without Implementation Cost
• Authors: Shunya Minami, Song Liu, Stephen Wu, Kenji Fukumizu, Ryo Yoshida
• Abstract summary: We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. We demonstrate its simplicity, generality, and applicability using various real data applications.
• Score: 18.224991762123576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 regression 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 variable. We demonstrate its simplicity, generality, and applicability using various real data applications.

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