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    Generative models· 2022

    Flow Matching for Generative Modeling

    Yaron Lipman (first author) · 2022 · published 2023

    Read the original

    arXiv 2210.02747OpenAlex W4303647933

    On the reading lists

    Why it matters

    Established simulation-free continuous-flow training, enabling scalable transport-based generative models.

    Condensed from the expert lists' notes.

    The record

    First released
    2022 (from arXiv 2210.02747)
    Published
    2023
    Citations
    7,003 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.609 · computed rank 140 · consensus 0.86, must-read 1, citations percentile 0.718 (2020s · generative-models), awards 0, existing lists 0. Method

    Editor's notes

    • Readability check: pass: A clean framework now behind many image models; mathematical but clear. (AI draft (Claude), not yet reviewed, 2026-10-29)

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.