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Uniform electric field8/31/2023 The forward/backward ODEs evolved a distribution (top) or a (augmented) sample (bottom) in the Poisson field. As shown in the figure below, motion in a viscous fluid converts any planar charge distribution into a uniform angular distribution. They interpret N-dimensional data items x (say, pictures) as positive electric charges in the z = 0 plane of an N+1-dimensional environment filled with a viscous liquid like honey. ![]() We introduce a novel “Poisson flow” generative model (PFGM) that takes advantage of a surprising physics fact that extends to N dimensions. ![]() However, these approaches have yet to outperform their SDE equivalents. They suggest backward ODEsamplers (normalizing flow) accelerate the sampling process. New strategies for securing the training of CNN-based or ViT-based GAN models are presented. Although recent developments in diffusion and scored-based models attain equivalent sample quality to GANs without adversarial training, the stochastic sampling procedure in these models is sluggish. ![]() Current deep generative models, however, have shortcomings such as unstable training objectives (GANs) and low sample quality (VAEs, normalizing flows ). Deep generative models are a popular data generation strategy used to generate high-quality samples in pictures, text, and audio and improve semi-supervised learning, domain generalization, and imitation learning.
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