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Score-Based Generative Model/Evidence
Method evidence record

Score-Based Generative Model

A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Score-Based Generative Modeling through Stochastic Differential Equations
Taxonomic method record · ml-model / deep-learning
  • Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907. · URL
  • Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR. · URL
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Related methods

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Same method familyCapsule Networkmachine-suggested · Relational suggestion, not evidence.Same method familyDeep Reinforcement Learningmachine-suggested · Relational suggestion, not evidence.Same method familyNeural ODEmachine-suggested · Relational suggestion, not evidence.Same method familyPrincipal Component Analysismachine-suggested · Relational suggestion, not evidence.Same method familyVariational Autoencodermachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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