Adversarial Training Improves Joint Energy-Based Generative Modelling

Rostislav Korst; Arip Asadulaev

Adversarial Training Improves Joint Energy-Based Generative Modelling

Machine Learning 2022-07-20 v1

Authors: Rostislav Korst , Arip Asadulaev

Abstract

We propose the novel framework for generative modelling using hybrid energy-based models. In our method we combine the interpretable input gradients of the robust classifier and Langevin Dynamics for sampling. Using the adversarial training we improve not only the training stability, but robustness and generative modelling of the joint energy-based models.

Keywords

adversarial training generative adversarial network adversarial examples

Cite

@article{arxiv.2207.08950,
  title  = {Adversarial Training Improves Joint Energy-Based Generative Modelling},
  author = {Rostislav Korst and Arip Asadulaev},
  journal= {arXiv preprint arXiv:2207.08950},
  year   = {2022}
}

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