提出联合时空差异的训练方法，提升能量模型在图像和分子数据上的密度估计性能。
Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences
- 利用时空联合差异替代仅空间或时间差异，统一现有方法
- 在图像和分子数据上达到与顶尖方法相当的密度估计精度
- 适用于需要高精度生成建模的研究者，如分子结构生成
从数据样本中学习能量模型是机器学习的核心问题。近年来许多流行方法，如用于训练能量扩散模型的去噪得分匹配，使用随机插值在不同噪声水平下扰动数据样本，通过时间变量定义数据空间与时间的联合分布。多数方法通过空间或时间差异来学习该联合分布的能量。我们识别出这两种方法各自存在的失效模式。为解决这些问题，本文提出时空噪声对比估计（stNCE），一种通过联合时空差异学习能量的框架。stNCE统一了多种现有方法，并导出新的训练目标。在图像和分子数据上的实验表明，其性能可媲美当前最优密度估计方法。
原文摘要 · Abstract (English)
Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-based diffusion models, use stochastic interpolants to corrupt data samples at different noise levels indexed by a time variable. This defines a joint density over both the data space and time, and most methods learn its energy through either spatial or temporal differences. We identify distinct failure modes for both of these approaches. To solve them, we propose Spatiotemporal Noise-Contrastive Estimation (stNCE), a framework for learning the energy through joint spatiotemporal differences. stNCE unifies many existing methods and leads to new training objectives. Experiments on images and molecules demonstrate performance competitive with state-of-the-art density estimation methods.
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