Characteristic Learning for Provable One Step Generation

We propose the characteristic generator, an one-step generative model that combines the sampling efficiency of generative adversarial networks (GANs) with the training stability of flow-based models. The proposed model is based on characteristics along which probability-density transport is governed by ordinary differential equations (ODEs). Specifically, we first estimate the underlying velocity field and numerically solve the probability-flow ODE using an exponential integrator, thereby obtaining discrete approximations of the characteristic trajectories. We then train a deep neural network to approximate these trajectories, yielding a one-step transport map from a Gaussian prior to the target distribution. Theoretically, we analyze the errors introduced by velocity-field estimation, numerical discretization, and characteristic approximation, and establish a nonasymptotic convergence rate in the Wasserstein-2 distance under mild assumptions on the data distribution. Moreover, under a low-dimensional linear-subspace assumption, we show that the convergence rate depends on the intrinsic data dimension rather than the potentially much larger ambient dimension, demonstrating the model's ability to alleviate the curse of dimensionality. Experiments on synthetic and real-world datasets show that the characteristic generator produces high-quality, high-resolution samples using only one or a few neural-network evaluations.