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From physics to generative AI: An AI model for advanced pattern generation | MIT News | Massachusetts Institute of Technology

Drawing inspiration from physics, a new Poisson Flow Generative Model ++ (PFGM++) integrates diffusion and Poisson Flow principles, outperforming existing diffusion models in advanced image generation. This breakthrough in generative AI taps into both the complexity of electric fields and the simplicity of diffusion to create realistic patterns and images with potential applications spanning multiple domains. ....

Max Tegmark , Jesse Thaler , Yilun Xu , Ziming Liu , Rothberg Family Fund For Cognitive Science , Google Senior Research Scientist Yonglong Tian Ph , International Conference On Machine , National Science Foundation , Department Of Electrical Engineering , Synthesis Consortium , Foundational Questions Institute , Laboratory For Nuclear Science Center , Senior Research Scientist Karsten Kreis , Artificial Intelligence Laboratory , Family Foundation , Department Of Physics , Poisson Flow , Flow Generative Model , Nuclear Science , Theoretical Physics , Artificial Intelligence , Fundamental Interactions , Frechet Inception Distance , Yang Song , Flow Generative Models , Research Scientist Karsten Kreis ,

A simpler path to better computer vision

Research finds using a large collection of simple, un-curated synthetic image generation programs to pretrain a computer vision model for image classification yields greater accuracy than employing other pretraining methods that are more costly and time consuming, and less scalable. ....

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