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Learning Through Energy Refinement and Manifold Projection: A Cooperative EBM-AE Framework

arXiv · AI, language, vision and robotics · article · Sep 12, 2026 · UTC

Energy-Based Models (EBMs) provide a flexible framework for generative modeling by learning an energy landscape that assigns low energy values to realistic samples and higher energies to unlikely observations. Despite their theoretical appeal, training EBMs remains challenging due to the computational cost of Langevin sampling and the difficulty of efficiently exploring the learned data manifold. In this work, we propose a cooperative Energy-Based Model and Autoencoder (EBM-AE) framework that combines energy-based refinement with manifold projection. The proposed approach jointly trains an EBM

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