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Denoising score matching (DSM) requires that the training data must come from the target distribution, otherwise the network learns the score of the wrong distribution, which is a fundamental obstacle to applying diffusion models to lattice field theory.
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10/14/2026
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UnverifiedIn diffusion models, the score function is used to describe the log gradient of the data distribution and is trained via denoising score matching.75% similarVerifiedThe denoising process of diffusion models is based on the DDPM principle, which involves progressively adding Gaussian noise to data during training and then training a neural network to learn the reverse denoising process.73% similarUnverifiedThe fundamental issue of cross-task calibration is that the model's probability output is learned on a specific training distribution; when the input distribution changes, the reliability of the probabilities drifts but the model cannot perceive it.73% similarUnverifiedFlow matching models generate images by learning a continuous probability flow (ODE trajectory) from the noise distribution to the data distribution, and are generally more stable to train and require fewer inference steps.72% similarUnverifiedCurrent mainstream diffusion models tend to select the highest-probability denoising path at each step, causing generated results to gravitate toward the statistical center of the training data distribution and exhibit the most averaged visual presentation.71% similar
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