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Models

Models implemented in dpmhm:

  • Supervised learning: VGGish networks
  • Unsupervised learning: Auto-encoder and VAE
  • Self-supervised learning: contrastive and generative models
  • Semi-supervised learning: hybrid models targeting transfer learning.

Supervised learning

For labelled datasets only, representations can be learned by solving the classification task. The following models are implemented:

  • VGGish12
  • VGGish for signal (WIP)

Unsupervised learning

UL can be applied on datasets without label about the faulty state. The following models are implemented:

  • Auto-encoder 34
  • Monte-Carlo EM
  • Variational Auto-encoder (VAE) 56

Self-supervised learning

SSL models can be divided into contrastive and generative ones. The following models are implemented:

  • SimCLR7

Semi-supervised learning

  1. Karen Simonyan and Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs], September 2014. arXiv: 1409.1556. URL: http://arxiv.org/abs/1409.1556 (visited on 2019-03-29). ↩

  2. Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F. Gemmeke, Aren Jansen, R. Channing Moore, Manoj Plakal, Devin Platt, Rif A. Saurous, and Bryan Seybold. CNN architectures for large-scale audio classification. In 2017 ieee international conference on acoustics, speech and signal processing (icassp), 131–135. IEEE, 2017. ↩

  3. Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol, and Léon Bottou. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of machine learning research, 2010. ↩

  4. Quan Qian, Yi Qin, Yi Wang, and Fuqiang Liu. A new deep transfer learning network based on convolutional auto-encoder for mechanical fault diagnosis. Measurement, 178:109352, 2021. Publisher: Elsevier. ↩

  5. Diederik P. Kingma and Max Welling. Auto-Encoding Variational Bayes. arXiv:1312.6114 [cs, stat], December 2013. arXiv: 1312.6114. URL: http://arxiv.org/abs/1312.6114 (visited on 2019-07-14). ↩

  6. Jinwon An and Sungzoon Cho. Variational autoencoder based anomaly detection using reconstruction probability. Special lecture on IE, 2(1):1–18, 2015. ↩

  7. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A Simple Framework for Contrastive Learning of Visual Representations. In International Conference on Machine Learning, 1597–1607. PMLR, November 2020. ISSN: 2640-3498. URL: http://proceedings.mlr.press/v119/chen20j.html (visited on 2021-07-13). ↩