Product quality prediction method in small sample data environment

Liu, Feixiang; Dai, Ye

doi:10.1016/j.aei.2023.101975

Cited by 24 publications

(3 citation statements)

References 34 publications

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“…In the literature, the solutions to few-shot learning include domain generalization 11 and transfer learning 12 . After the application of these methods in other fields, including object detection 13 and fault classification 14 , there have been some successful examples of few-shot learning methods 15 in production quality prediction. However, in most cases, the above model can only be trained from scratch, and then fine-tuning is used to learn new tasks, which limits their adaptability in actual industrial production.…”

Section: Introductionmentioning

confidence: 99%

Meta learning based residual network for industrial production quality prediction with limited data

Shi,

Cao,

Chen

et al. 2024

Sci Rep

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Due to the challenge of collecting a substantial amount of production-quality data in real-world industrial settings, the implementation of production quality prediction models based on deep learning is not effective. To achieve the goal of predicting production quality with limited data and address the issue of model degradation in the training process of deep learning networks, we propose Meta-Learning based on Residual Network (MLRN) models for production quality prediction with limited data. Firstly, the MLRN model is trained on a variety of learning tasks to acquire knowledge for predicting production quality. Furthermore, to obtain more features with limited data and avoid the issues of gradient disappearing or exploding in deep network training, the enhanced residual network with the effective channel attention (ECA) mechanism is chosen as the basic network structure of MLRN. Additionally, a multi-batch and multi-task data input approach is implemented to prevent overfitting. Finally, the availability of the MLRN model is demonstrated by comparing it with other models using both numerical and graphical datasets.

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Section: Introductionmentioning

confidence: 99%

Meta learning based residual network for industrial production quality prediction with limited data

Shi,

Cao,

Chen

et al. 2024

Sci Rep

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“…Data with errors, inconsistencies, or biases can adversely affect the performance of the transfer learning model. Therefore, it is important to ensure that the training data, both for pretraining and fine-tuning, is of high quality, accurately labeled, and representative of the target task [ 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Lobo

González

Boto

et al. 2023

IJMS

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Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.

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“…To tackle this issue, several research studies have put forth various solutions. Among these approaches, a direct and effective method involves utilizing generative networks for data augmentation [1]- [3]. By leveraging generative networks, the dataset can be substantially expanded, enabling more comprehensive training of downstream models.…”

Section: Introductionmentioning

confidence: 99%

LarGAN: A Label Auto-Rescaling Generation Adversarial Network for Rare Surface Defects

Zhang,

Qin,

2023

Preprint

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The emergence of single-image generation (SIG) has opened up new possibilities for generative models, making it feasible to generate small datasets that were previously impractical. This paper presents LarGAN, a generative model designed specifically for generating images of rare defects, such as casting slabs, and explores its utility in the context of data augmentation and defect detection tasks. LarGAN model leverages a progressive training framework and an adaptive label auto-scaling method to produce defect images that closely resemble the input image, using only a single defect image as input. The results of the experiments demonstrate that LarGAN outperforms other single-image generative models in terms of both image quality and diversity. Moreover, the generated data can supplement the missing independent data distribution in the original dataset, rendering it particularly suitable for data augmentation and defect detection tasks, particularly when the availability of sample data is limited. Additionally, the experimental results indicate that the data generated by LarGAN can effectively augment the feature space of the original dataset, thereby improving the accuracy and generalization performance of the detection model. As such, this study provides a valuable generation method for detection models utilized in industrial contexts that require substantial amounts of data.

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Product quality prediction method in small sample data environment

Cited by 24 publications

References 34 publications

Meta learning based residual network for industrial production quality prediction with limited data

Meta learning based residual network for industrial production quality prediction with limited data

Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

LarGAN: A Label Auto-Rescaling Generation Adversarial Network for Rare Surface Defects

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