A Novel Ensemble Strategy Based on Determinantal Point Processes for Transfer Learning

Ying Lv, Bofeng Zhang (), Xiaodong Yue and Zhikang Xu
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Ying Lv: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Bofeng Zhang: School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China
Xiaodong Yue: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Zhikang Xu: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Mathematics, 2022, vol. 10, issue 23, 1-15

Abstract: Transfer learning (TL) hopes to train a model for target domain tasks by using knowledge from different but related source domains. Most TL methods focus more on improving the predictive performance of the single model across domains. Since domain differences cannot be avoided, the knowledge from the source domain to obtain the target domain is limited. Therefore, the transfer model has to predict out-of-distribution (OOD) data in the target domain. However, the prediction of the single model is unstable when dealing with the OOD data, which can easily cause negative transfer. To solve this problem, we propose a parallel ensemble strategy based on Determinantal Point Processes (DPP) for transfer learning. In this strategy, we first proposed an improved DPP sampling to generate training subsets with higher transferability and diversity. Second, we use the subsets to train the base models. Finally, the base models are fused using the adaptability of subsets. To validate the effectiveness of the ensemble strategy, we couple the ensemble strategy into traditional TL models and deep TL models and evaluate the transfer performance models on text and image data sets. The experiment results show that our proposed ensemble strategy can significantly improve the performance of the transfer model.

Keywords: transfer learning; ensemble strategy; determinantal point processes; domain adaptation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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