ANTI-AI ARCHIVE
ART-HISTORY NODE / 077 · 2021-05

Diffusion Models Beat GANs

Diffusion Models Beat GANs

Dhariwal & Nichol

Historical context & research account

Architectural improvements and classifier guidance allow diffusion models to outperform prominent GAN approaches on ImageNet synthesis. The master identifies this as a technical turning point in the transition from GAN-centered image generation toward diffusion. The comparison concerns a particular research benchmark and period, rather than every possible artistic or technical application.

Dates & version record

Date displayed for this node:2021-05 · Date recorded in the research master:2021-05

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Original sources & further reading

These links lead to the cited paper, article, institution or conference page. External texts retain their source languages.

01
Diffusion Models Beat GANs ↗

Diffusion Models Beat GANs

https://proceedings.neurips.cc/paper/2021/hash/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html

Current cited URL

02
Diffusion Models Beat GANs ↗

Diffusion Models Beat GANs

https://arxiv.org/abs/2105.05233

URL cited in a research master or supplement; it may have moved or expired

Provenance, translation & verification

Archive node #077 · Historical node

Research masters & supplement references · 3

AI Art Genealogy Research Master · p. 23 · 077
Date as recorded: 2021-05
Diffusion Models Beat GANs ↗
Diffusion Models Beat GANs

GenAI Visual Release Chronology Research Master · p. 2 · R005
Date as recorded: 2021-05
Diffusion Models Beat GANs ↗
Dhariwal & Nichol — Diffusion Models Beat GANs

GenAI Visual Release Chronology Research Master · p. 2 · R006
Date as recorded: 2021-05
Diffusion Models Beat GANs ↗
Diffusion Models Beat GANs

Archive account adapted from the masters, not a full translation of the linked work. AI-assisted translation; human review pending.

The master’s research claims and dates await independent verification.

Cite this node

ANTI-AI ARCHIVE. Diffusion Models Beat GANs. Art-history node #077. https://salondesrefuses.cn/en/art-history/077

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