AI RESEARCH
Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
arXiv CS.LG
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ArXi:2510.13872v4 Announce Type: replace Simultaneously achieving robust classification and high-fidelity generative modeling within a single framework presents a significant challenge. Hybrid approaches, such as Joint Energy-Based Models (JEM), interpret classifiers as EBMs but are often limited by the instability and poor sample quality inherent in