论文标题
一般的转移学习回归，而无需实施成本
A General Class of Transfer Learning Regression without Implementation Cost
论文作者
论文摘要
我们提出了一个新颖的框架，该框架统一并扩展了现有的转移学习方法（TL）进行回归。为了在目标任务上桥接一个验证的源模型，我们引入了密度比率重新加权函数，该功能通过贝叶斯框架估计，具有特定的先验分布。通过更改两个固有的超参数和密度比率模型的选择，提出的方法可以基于跨域相似性正规化，使用密度比率估计的概率TL整合TL：TL的三种流行方法，并使用预期的神经网络进行微调。此外，提出的方法可以从其简单的实施中受益，而无需任何额外的成本；可以使用现成的库进行充分训练回归模型，以进行监督学习，其中原始输出变量简单地转换为新的输出变量。我们使用各种实际数据应用程序证明了它的简单性，一般性和适用性。
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.
