Large-scale analysis of Artbreeder remix parties finds images simplify and converge on thematic attractors, with trade-offs between novelty, complexity, and party size in human-AI creative lineages.
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A dual ascent optimization framework is introduced for MAP estimation with diffusion priors, claimed to outperform prior methods on image restoration in quality, noise robustness, speed, and data fidelity.
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Dynamics of collective creativity in AI art competitions
Large-scale analysis of Artbreeder remix parties finds images simplify and converge on thematic attractors, with trade-offs between novelty, complexity, and party size in human-AI creative lineages.
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Dual Ascent Diffusion for Inverse Problems
A dual ascent optimization framework is introduced for MAP estimation with diffusion priors, claimed to outperform prior methods on image restoration in quality, noise robustness, speed, and data fidelity.