# Learning Energy-Based Models With Adversarial Training

arXiv: https://arxiv.org/abs/2012.06568 · HTML: https://mlsanity.com/paper/2012.06568/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Energy-guided Entropic Neural Optimal Transport](https://mlsanity.com/paper/2304.06094/) (2023)
- [Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood](https://mlsanity.com/paper/2309.05153/) (2023)
- [EGC: Image Generation and Classification via a Diffusion Energy-Based Model](https://mlsanity.com/paper/2304.02012/) (2023)
- [Mapping New Realities: Ground Truth Image Creation with Pix2Pix Image-to-Image Translation](https://mlsanity.com/paper/2404.19265/) (2024)
- [Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey](https://mlsanity.com/paper/2404.05219/) (2024)
- [Your Classifier Can Do More: Towards Balancing the Gaps in Classification, Robustness, and Generation](https://mlsanity.com/paper/2505.19459/) (2025)
- [Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation](https://mlsanity.com/paper/2510.13872/) (2025)
- [A Provable Energy-Guided Test-Time Defense Boosting Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2603.26984/) (2026)
- [Generative Robust Classification](https://mlsanity.com/paper/2212.07283/) (2022)

## Abstract (arXiv)

We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT). We show that (binary) AT learns a special kind of energy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning of EBMs. We further propose improved techniques for generative modeling with AT, and demonstrate that this new approach is capable of generating diverse and realistic images. Aside from having competitive image generation performance to explicit EBMs, the studied approach is stable to train, is well-suited for image translation tasks, and exhibits strong out-of-distribution adversarial robustness. Our results demonstrate the viability of the AT approach to generative modeling, suggesting that AT is a competitive alternative approach to learning EBMs.