Historical context & research account
StackGAN uses a two-stage process that first turns a description into low-resolution structure and then refines it into a higher-resolution image. It is a significant branch of text-to-image research before DALL·E and Imagen, demonstrating that prompt-driven image generation has a technical history preceding diffusion models.
Dates & version record
Date displayed for this node：2016–2017 · Date recorded in the research master：2016–2017
These dates refer to the historical event or recorded version, not this page’s publication date. The original date precision and unresolved questions are retained.
Original sources & further reading
These links lead to the cited paper, article, institution or conference page. External texts retain their source languages.
StackGAN: Text to Photo-realistic Image Synthesis with Stacked GANs
https://arxiv.org/abs/1612.03242
Current cited URL
Provenance, translation & verification
Archive node #054 · Historical node
Research masters & supplement references · 1
AI Art Genealogy Research Master · p. 17 · 054
Date as recorded: 2016–2017
StackGAN: Text to Photo-realistic Image Synthesis with Stacked GANs ↗
StackGAN: Text to Photo-realistic Image Synthesis with Stacked 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. StackGAN: Text to Photo-realistic Image Synthesis with Stacked GANs. Art-history node #054. https://salondesrefuses.cn/en/art-history/054
For specific historical claims, also cite the original sources above and include your access date. This account is not a full translation of the linked work.
Adjacent nodes follow chronological order; adjacency does not establish direct influence or causation.