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Diffusion Model/Evidence
Method evidence record

Diffusion Model

A diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.

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Source record

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

Denoising Diffusion Probabilistic Model (DDPM / Latent Diffusion)
Taxonomic method record · ml-model / deep-learning
  • Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. · URL
  • Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. CVPR. · URL
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Related methods

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Same method familyNeural ODEmachine-suggested · Relational suggestion, not evidence.Same method familyPrincipal Component Analysismachine-suggested · Relational suggestion, not evidence.Same method familyScore-Based Generative Modelmachine-suggested · Relational suggestion, not evidence.Same method familyVariational Autoencodermachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

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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