「流匹配」成ICML 2025超热门主题！网友：都说了学物理的不准转计算机

Core Viewpoint - The article discusses the emerging significance of Flow Matching technology in the field of generative AI, highlighting its connection to fluid dynamics and its potential to enhance model quality and stability [4][5][8]. Group 1: Flow Matching Technology - Flow Matching technology is gaining attention for its ability to address key elements in generative AI, such as quality, stability, and simplicity [5]. - The FLUX model has catalyzed interest in Flow Matching architectures that can handle various input types [6]. - Flow Matching is based on Normalizing Flows (NF), which gradually maps complex probability distributions to simpler ones through a series of reversible transformations [18]. Group 2: Relationship with Fluid Dynamics - The core concept of Flow Matching is derived from fluid dynamics, particularly the continuity equation, which emphasizes that mass cannot be created or destroyed [22][23]. - Flow Matching focuses on the average density of particles in a space, paralleling how it tracks the transition from noise distribution to data distribution [20][25]. - The process involves defining a velocity field that guides the transformation from noise to data, contrasting with traditional methods that start from particle behavior [24][25]. Group 3: Generative Process - The generative process in Flow Matching involves mapping noise to data through interpolation, where the model learns to move samples along a defined path [12][17]. - The method emphasizes the average direction of paths leading to high-probability samples, allowing for effective data generation [30][34]. - Flow Matching can be seen as a special case of diffusion models when Gaussian distribution is used as the interpolation strategy [41]. Group 4: Comparison with Diffusion Models - Flow Matching and diffusion models share similar forward processes, with Flow Matching being a subset of diffusion models [40]. - The training processes of both models exhibit equivalence when Gaussian distributions are employed, although Flow Matching introduces new output parameterization as a velocity field [35][44]. - The design of weight functions in Flow Matching aligns closely with those commonly used in diffusion model literature, impacting the model's performance [45].