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Whereas generative adversarial networks are capable of synthesizing highly realistic images of faces, cats, landscapes, or almost any other single category, paint-by-text synthesis engines can -- from a single text prompt -- synthesize realistic images of seemingly endless categories with arbitrary configurations and combinations.
Maximum likelihood from incomplete data via the EM algorithm
Arthur P. Dempster, Nan M.. Laird, and Donald B Rubin · 1977
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David Bau, Alex Andonian, Audrey Cui, YeonHwan Park, Ali Jahanian, Aude Oliva, and Antonio Torralba · 2021
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Learning an animatable detailed 3D face model from in-the-wild images
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Surreal or too real? Breathtaking AI tool DALL-E takes its images to a bigger stage
Bobby Allyn · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu · 2022
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Perspective (in)consistency of paint by text
Hany Farid · 2022
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