An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling

Liu, Han; Zhang, Feng; Zhang, Xiaolin; Zhao, Siyang

doi:10.48550/arxiv.2110.13691

2021

DOI: 10.48550/arxiv.2110.13691

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An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling

Han Liu¹,

Feng Zhang²,

Xiaolin Zhang³

et al.

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One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning

2022

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The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Fault diagnosis of wind turbines is beneficial to improve the reliability of wind turbines. Due to various reasons, such as difficulty in obtaining fault data, random changes in operating conditions, or compound faults, many deep learning algorithms show poor performance. When fault samples are small, ordinary deep learning will fall into overfitting. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples. A novel method based on meta-analogical momentum contrast learning (MA-MOCO) is proposed in this paper to solve the problem of the very few samples of wind turbine failures, especially one-shot. By improving the momentum contrast learning (MOCO) and using the training idea of meta-learning, the one-shot fault diagnosis of wind turbine drivetrain is analyzed. The proposed model shows a higher accuracy than other common models (e.g., model-agnostic meta-learning and Siamese net) in one-shot learning. The feature embedding is visualized by t-distributed stochastic neighbor embedding (t-SNE) in order to test the effectiveness of the proposed model.

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One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning

2022

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An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling

Cited by 1 publication

References 26 publications

One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning

One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning

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