Instance-based transfer learning method via modified domain-adversarial neural network with influence function: Applications to design metamodeling and fault diagnosis

The availability of a large amount of high-quality data is critical to the performance of machine learning models. It is challenging to obtain a training dataset because data collection is costly and time-consuming. However, data scarcity can be overcome and an accurate model can be obtained if data from similar models are reused. In this paper, we propose an instance-based transfer learning method to obtain a more accurate model for situations with data scarcity. The proposed method uses a modified domain-adaptation technique to generate auxiliary target-domain data from source-domain data. Subsequently, useful data are selected from the auxiliary target-domain data to preclude the negative transfer that may leverage source-domain data to reduce the learning performance in the target domain. A modified domain-adversarial neural network was used to generate auxiliary target domain data in the context of instance-based transfer learning. Particularly, the feature extractor and domain discriminator were trained to extract the domain-invariant features from the source and target domains, whereas the target generator was trained to generate auxiliary target-domain data using the domain-invariant features. Additionally, an influence function that can measure the influence of individual training samples on the learning performance was applied to identify useful data. Three case studies were conducted to validate the proposed method: a mathematical function example, drone blade metamodeling, and bearing fault diagnosis. The results of these case studies indicate a significant improvement in neural network prediction despite data scarcity. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

The availability of high-quality data is crucial for the performance of machine learning models. This paper proposes an instance-based transfer learning method to overcome data scarcity and improve model accuracy by reusing data from similar models. The results of three case studies indicate a significant improvement in neural network prediction despite data scarcity.