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 based deep generative method for learning conditional distributions%2C named Conditional Follmer Flow. Starting from a standard Gaussian distribution%2C the proposed flow could approximate the target conditional distribution very well when the time is close to 1. For effective implementation%2C we discretize the flow with Euler's method where we estimate the velocity field nonparametrically using a deep neural network. Furthermore%2C we also establish the convergence result for the Wasserstein-2 distance between the distribution of the learned samples and the target conditional distribution%2C providing the first comprehensive end-to-end error analysis for conditional distribution learning via ODE flow. Our numerical experiments showcase its effectiveness across a range of scenarios%2C from standard nonparametric conditional density estimation problems to more intricate challenges involving image data%2C illustrating its superiority over various existing conditional density estimation methods.?quality=80&w=800)
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