Historical context & research account
AttnGAN aligns words with visual regions through attention and a deep attentional multimodal similarity model, improving details corresponding to complex descriptions. It provides an important pre-diffusion example of the problems now discussed as adherence to prompts and alignment between textual and visual information.
Dates & version record
Date displayed for this node：2017–2018 · Date recorded in the research master：2017–2018
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Original sources & further reading
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AttnGAN: Fine-Grained Text to Image Generation with Attentional GANs
https://arxiv.org/abs/1711.10485
Current cited URL
Provenance, translation & verification
Archive node #059 · Historical node
Research masters & supplement references · 1
AI Art Genealogy Research Master · p. 19 · 059
Date as recorded: 2017–2018
AttnGAN: Fine-Grained Text to Image Generation with Attentional GANs ↗
AttnGAN: Fine-Grained Text to Image Generation with Attentional GANs
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Cite this node
ANTI-AI ARCHIVE. AttnGAN: Fine-Grained Text to Image Generation with Attentional GANs. Art-history node #059. https://salondesrefuses.cn/en/art-history/059
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