An analytic theory of creativity in convolutional diffusion models
from Arxiv Papers Hosted by Igor Melnyk
The paper presents a predictive theory of creativity in convolutional diffusion models, identifying inductive biases that enable novel image generation beyond training data through local patch combinations. https://arxiv.org/abs//2412.20292 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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This episode is 22 minutes long.
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This episode was published on January 4, 2025.
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