2 citations · 2 across the 1 of their papers we have counts for
4 papers
First Learn, Then Memorize: The Spectral Bias of Diffusion Models
Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1
Diffusion models trained on a finite dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set. We identify the mechanism beh…
Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data
Jérémie Klinger, Raphaël Urfin, Giulio Biroli +1
Score-based generative models generate new samples by integrating a time-dependent drift that carries Gaussian noise onto the target distribution. In practice this drift is modeled…
Double Descent and Malign Overfitting in Diffusion Models
Raphaël Urfin, Tony Bonnaire, Giulio Biroli +1
Conventional wisdom in deep learning holds that overparameterization---having more parameters than training samples ---is benign: larger models generalize better and, even w…
Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training
Tony Bonnaire, Raphaël Urfin, Giulio Biroli +1
Diffusion models have achieved remarkable success across a wide range of generative tasks. A key challenge is understanding the mechanisms that prevent their memorization of traini…