From the source
From the source
Google Research published a paper showing that the creativity of diffusion models is a mathematical consequence of neural networks learning a smoothed version of the score function, which causes interpolation between training data points.
From the source
We show that a diffusion model’s creativity (its ability to generate novel data, rather than just memorize its training set) is a mathematical consequence of neural networks learning a "smoothed" version of the score function, driving the model to interpolate between training data points along the hidden data manifold.
research.google