A General Class of Transfer Learning Regression without Implementation Cost
| Autor: | Minami, Shunya, Liu, Song, Wu, Stephen, Fukumizu, Kenji, Yoshida, Ryo |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 35:8992-8999 |
| ISSN: | 2374-3468 2159-5399 |
| Popis: | 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. 31 pages, 6 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|