Imbalanced data augmentation for pipeline fault diagnosis: A multi-generator switching adversarial network

Author:

Shang Rou,Dong HongliORCID,Wang Chuang,Chen Shuangqing,Sun Tong,Guan Chuang

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Reference54 articles.

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3. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. In Proceedings of the international conference on machine learning (pp. 214–223). Sydney, Australia.

4. Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems (NIPS). Barcelona, Spain.

5. Training generative neural networks via maximum mean discrepancy optimization;Dziugaite,2015

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