用随机对称化构建轻量级等变扩散模型，提升分子生成效果。
SymDiff: Equivariant Diffusion via Stochastic Symmetrisation
- 采样时引入随机对称化，无需复杂等变网络
- 在E(3)等变分子生成中表现显著优于传统方法
- 适配主流架构，适合快速部署于生成任务
我们提出SymDiff，一种基于随机对称化框架构建等变扩散模型的方法。SymDiff在采样时表现为可学习的数据增强，轻量、高效且易于在任意现成模型上实现。与以往工作不同，它通常无需固有等变的神经网络组件，避免了复杂的参数化或高阶几何特征的使用。相反，该方法可直接采用高度可扩展的现代架构作为替代，带来显著的实证优势，尤其在E(3)等变分子生成任务中。据我们所知，这是首次将对称化应用于生成建模，暗示其在该领域具有更广泛潜力。
原文摘要 · Abstract (English)
We propose SymDiff, a method for constructing equivariant diffusion models using the framework of stochastic symmetrisation. SymDiff resembles a learned data augmentation that is deployed at sampling time, and is lightweight, computationally efficient, and easy to implement on top of arbitrary off-the-shelf models. In contrast to previous work, SymDiff typically does not require any neural network components that are intrinsically equivariant, avoiding the need for complex parameterisations or the use of higher-order geometric features. Instead, our method can leverage highly scalable modern architectures as drop-in replacements for these more constrained alternatives. We show that this additional flexibility yields significant empirical benefit for $\mathrm{E}(3)$-equivariant molecular generation. To the best of our knowledge, this is the first application of symmetrisation to generative modelling, suggesting its potential in this domain more generally.
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