Guided Flows for Generative Modeling and Decision Making.
CoRR(2023)
摘要
Classifier-free guidance is a key component for improving the performance of
conditional generative models for many downstream tasks. It drastically
improves the quality of samples produced, but has so far only been used for
diffusion models. Flow Matching (FM), an alternative simulation-free approach,
trains Continuous Normalizing Flows (CNFs) based on regressing vector fields.
It remains an open question whether classifier-free guidance can be performed
for Flow Matching models, and to what extent does it improve performance. In
this paper, we explore the usage of Guided Flows for a variety of downstream
applications involving conditional image generation, speech synthesis, and
reinforcement learning. In particular, we are the first to apply flow models to
the offline reinforcement learning setting. We also show that Guided Flows
significantly improves the sample quality in image generation and zero-shot
text-to-speech synthesis, and can make use of drastically low amounts of
computation without affecting the agent's overall performance.
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