贪婪即美德：引导生成的统一视角
Greed is Good: A Unifying Perspective on Guided Generation
AI总结：
本文通过将后验引导视为端到端引导的贪婪策略，统一了两种梯度引导方法，并提出了在计算与精度之间权衡的插值方法，在逆图像问题和分子生成任务上验证了有效性。
摘要：
无训练引导生成是一种广泛使用且强大的技术，允许最终用户对流/扩散模型的生成过程施加进一步控制。一般来说，针对基于梯度的引导，已经出现了两种技术系列：即后验引导（即通过目标预测模型将当前样本投影到目标分布进行引导）和端到端引导（即通过在整个ODE求解过程中执行反向传播进行引导）。在这项工作中，我们表明这两个看似分离的系列实际上可以通过将后验引导视为端到端引导的贪婪策略来统一。我们探索了这两个系列之间的理论联系，并深入分析了这两种技术相对于连续理想梯度的关系。基于这一分析，我们提出了一种在这两个系列之间插值的方法，从而在引导梯度的计算与精度之间实现权衡。然后，我们在几个逆图像问题和性质引导的分子生成任务上验证了这项工作。
英文摘要：
Training-free guided generation is a widely used and powerful technique that allows the end user to exert further control over the generative process of flow/diffusion models. Generally speaking, two families of techniques have emerged for solving this problem for gradient-based guidance: namely, posterior guidance (i.e., guidance via projecting the current sample to the target distribution via the target prediction model) and end-to-end guidance (i.e., guidance by performing backpropagation throughout the entire ODE solve). In this work, we show that these two seemingly separate families can actually be unified by looking at posterior guidance as a greedy strategy of end-to-end guidance. We explore the theoretical connections between these two families and provide an in-depth theoretical of these two techniques relative to the continuous ideal gradients. Motivated by this analysis we then show a method for interpolating between these two families enabling a trade-off between compute and accuracy of the guidance gradients. We then validate this work on several inverse image problems and property-guided molecular generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
