mlsanityData to 7 Oct 2026

Adversarial Training Improves Joint Energy-Based Generative Modelling

arXiv 2207.08950 ↗First posted 18 Jul 2022cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 14 references that are in the map. First posted on arXiv 18 Jul 2022.

Builds on

10 of 14 references.

References · 10

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2016Improved Techniques for Training GANscited by 2,938
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  5. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
  6. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  7. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 402
  8. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 350
  9. 2019Residual Flows for Invertible Generative Modelingcited by 256
  10. 2019Adversarially robust transfer learningcited by 66

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2023Exploring the Connection between Robust and Generative Modelscited by 2
  2. 2025Your Classifier Can Do More: Towards Balancing the Gaps in Classification, Robustness, and Generationcited by 1
  3. 2025Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generationcited by 1