One-Step Generative Modeling via Wasserstein Gradient Flows 事件

One-Step Generative Modeling via Wasserstein Gradient Flows arXiv:2605.11755v2 Announce Type: replace-cross Abstract: Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it requires many iterative updates. We introduce W-Flow, a framework for training a generator that transforms samples from a simple reference distribution into samples from a target data distribution in a single step. This is achieve