用于生成建模与决策的 Guided Flows
Guided Flows for Generative Modeling and Decision Making
AI总结：
本文将无分类器引导引入 Flow Matching 模型形成 Guided Flows，在图像生成、零样本文本转语音及离线强化学习计划生成中实现最先进或可比性能，并较扩散模型获得10倍计算加速。
摘要：
无分类器引导是在各类任务中提升条件生成模型性能的关键组成部分。尽管它此前已在样本质量方面展现出显著改进，但其此前仅专门用于扩散模型。本文将无分类器引导集成到 Flow Matching（FM，流匹配）模型中；这是一种替代的无仿真方法，通过回归向量场来训练 Continuous Normalizing Flows（CNFs，连续归一化流）。我们探索了 Guided Flows 在多种下游应用中的使用。结果表明，Guided Flows 显著提升了条件图像生成和零样本文本转语音合成中的样本质量，并取得了最先进性能。值得注意的是，我们首次将流模型应用于离线强化学习设置中的计划生成，结果显示，与扩散模型相比，其计算速度提升 10 倍，同时保持相当的性能。
英文摘要：
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks. While it has previously demonstrated remarkable improvements for the sample quality, it has only been exclusively employed for diffusion models. In this paper, we integrate classifier-free guidance into Flow Matching (FM) models, an alternative simulation-free approach that trains Continuous Normalizing Flows (CNFs) based on regressing vector fields. We explore the usage of \emph{Guided Flows} for a variety of downstream applications. We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text-to-speech synthesis, boasting state-of-the-art performance. Notably, we are the first to apply flow models for plan generation in the offline reinforcement learning setting, showcasing a 10x speedup in computation compared to diffusion models while maintaining comparable performance.
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。
