Energy-based models (EBMs) exhibit a variety of desirable properties in predictive tasks, such as generality, simplicity and compositionality. However, training EBMs on high-dimensional datasets remains unstable and expensive. In this paper, we present a Manifold EBM (M-EBM) to boost the overall performance of unconditional EBM and Joint Energy-based Model (JEM). Despite its simplicity, M-EBM significantly improves unconditional EBMs in training stability and speed on a host of benchmark datasets, such as CIFAR10, CIFAR100, CelebA-HQ, and ImageNet 32x32. Once class labels are available, label-incorporated M-EBM (M-JEM) further surpasses M-EBM in image generation quality with an over 40% FID improvement, while enjoying improved accuracy. The code can be found at https://github.com/sndnyang/mebm.

翻译：基于能量的模型（EBM）在预测任务中展现出多种理想特性，如通用性、简洁性和组合性。然而，在高维数据集上训练EBM仍存在不稳定性且计算成本高昂。本文提出流形EBM（M-EBM）以提升无条件EBM与联合能量模型（JEM）的整体性能。尽管结构简洁，M-EBM在CIFAR10、CIFAR100、CelebA-HQ及ImageNet 32x32等多个基准数据集上显著改善了无条件EBM的训练稳定性与速度。若类别标签可用，标签增强型M-EBM（M-JEM）在图像生成质量上进一步超越M-EBM，FID提升超过40%，同时分类准确率也有所提高。代码详见https://github.com/sndnyang/mebm。