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Generative Modeling by Estimating Gradients of the Data Distribution

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If you have a question about this talk, please contact Randolf Altmeyer.

Existing generative models are typically based on explicit representations of probability distributions (e.g., autoregressive or VAEs) or implicit sampling procedures (e.g., GANs). We propose an alternative approach based on modeling directly the vector field of gradients of the data distribution (scores). Our framework allows flexible architectures, requires no sampling during training or the use of adversarial training methods. Additionally, score-based generative models enable exact likelihood evaluation through connections with normalizing flows. We produce samples comparable to GANs, achieving new state-of-the-art inception scores, and competitive likelihoods on image datasets.

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This talk is part of the CCIMI Seminars series.

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