无需训练即可引导流模型生成目标样本，直接在源空间做贝叶斯推断。
ESS-Flow: Training-free guidance of flow-based models as inference in source space
- 用椭圆切片采样在源空间直接做贝叶斯推断，不依赖梯度。
- 仅需前向传播，支持不可导的模拟或量化过程。
- 适用于材料设计和稀疏距离约束下的蛋白质结构预测。
引导预训练的流模型进行条件生成或生成具有特定目标属性的样本，可在无需成对数据重训练的情况下解决多种任务。我们提出 ESS-Flow，一种无梯度方法，利用流模型中通常为高斯分布的源分布，通过椭圆切片采样在源空间直接执行贝叶斯推断。ESS-Flow 仅需生成模型和观测过程的前向传播，无需梯度或雅可比计算，在生成或观测过程存在模拟、量化导致梯度不可靠或不可用时仍适用。我们在设计具有目标属性的材料以及从稀疏残基间距离测量值预测蛋白质结构的任务中验证了其有效性。
原文摘要 · Abstract (English)
Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on paired data. We present ESS-Flow, a gradient-free method that leverages the typically Gaussian prior of the source distribution in flow-based models to perform Bayesian inference directly in the source space using Elliptical Slice Sampling. ESS-Flow only requires forward passes through the generative model and observation process, no gradient or Jacobian computations, and is applicable even when gradients are unreliable or unavailable, such as with simulation-based observations or quantization in the generation or observation process. We demonstrate its effectiveness on designing materials with desired target properties and predicting protein structures from sparse inter-residue distance measurements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。