Research & academia
Towards demystifying the creativity of diffusion models
The article argues that diffusion models’ novelty can arise from score smoothing: neural networks learn a softened denoising function that lets generated data settle between training examples.
Why it matters: The proposed mechanism suggests a way to think about model design: score smoothing may help models generate novel samples without losing convergence toward meaningful data. The authors describe this as an initial effort, not a settled explanation for every setting.