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As explained in this video, flow-matching-based generative methods are a class of models that learn a "continuous vector field" in order to manage and transform what are relatively simple "noise distributions" into more complex data distributions. They do this by following ordinary differential equations. Instead of learning "discrete denoising steps" (that's what diffusion models do), they train the flow to match probability paths directly between data and noise.
Artificial intelligence




