提出新型损失函数，让能量模型生成更准、适用更广。
A Diffusive Classification Loss for Learning Energy-based Generative Models
- 将能量模型训练转为跨噪声水平的分类任务。
- 在高斯混合模型上逼近真实能量，避免模式遗漏。
- 适合需要精准采样与组合建模的研究者使用。
基于得分的生成模型近期取得显著进展。尽管通常以得分参数化，另一种方法是使用一系列时变的能量模型（EBM），其得分由能量对输入的负梯度得到。关键在于，EBM不仅可用于生成，还可用于组合采样或通过蒙特卡洛方法构建玻尔兹曼生成器。然而，训练EBM仍具挑战：直接最大似然因需嵌套采样而计算成本过高；虽得分匹配效率高，但存在模式遗漏问题。为此，我们提出扩散分类（DiffCLF）目标，一种简单且高效的方法，避免了模式遗漏。DiffCLF将EBM学习重构为跨噪声水平的监督分类问题，并可无缝结合标准得分目标。我们在解析的高斯混合案例中对比了估计能量与真实能量，验证了其有效性，并将训练好的模型应用于模型组合与玻尔兹曼生成器采样任务。结果表明，DiffCLF使EBM具有更高保真度和更广适用性。
原文摘要 · Abstract (English)
Score-based generative models have recently achieved remarkable success. While they are usually parameterized by the score, an alternative way is to use a series of time-dependent energy-based models (EBMs), where the score is obtained from the negative input-gradient of the energy. Crucially, EBMs can be leveraged not only for generation, but also for tasks such as compositional sampling or building Boltzmann Generators via Monte Carlo methods. However, training EBMs remains challenging. Direct maximum likelihood is computationally prohibitive due to the need for nested sampling, while score matching, though efficient, suffers from mode blindness. To address these issues, we introduce the Diffusive Classification (DiffCLF) objective, a simple method that avoids blindness while remaining computationally efficient. DiffCLF reframes EBM learning as a supervised classification problem across noise levels, and can be seamlessly combined with standard score-based objectives. We validate the effectiveness of DiffCLF by comparing the estimated energies against ground truth in analytical Gaussian mixture cases, and by applying the trained models to tasks such as model composition and Boltzmann Generator sampling. Our results show that DiffCLF enables EBMs with higher fidelity and broader applicability than existing approaches.
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