SeGMVAE

The main idea of the method in this paper comes from Meta-GMVAE.

CWRU fine-grained fault dataset

Baidu Netdisk link：https://pan.baidu.com/s/17iIKPcur3oNeTx8-NhRhLQ

提取码：523s

If it is helpful for your research, please kindly cite this work:



@inproceedings{lee2020meta,
  title={Meta-gmvae: Mixture of gaussian vae for unsupervised meta-learning},
  author={Lee, Dong Bok and Min, Dongchan and Lee, Seanie and Hwang, Sung Ju},
  booktitle={International Conference on Learning Representations},
  year={2020}
}

@article{ZHAO2024106482,
title = {A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis},
journal = {Neural Networks},
pages = {106482},
year = {2024},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2024.106482},
url = {https://www.sciencedirect.com/science/article/pii/S0893608024004064},
author = {Zhiqian Zhao and Yeyin Xu and Jiabin Zhang and Runchao Zhao and Zhaobo Chen and Yinghou Jiao}
}