arXiv Machine Learning
Sep 23

Double Descent and Malign Overfitting in Diffusion Models

The paper investigates why diffusion models, unlike typical deep learning models, exhibit catastrophic overfitting when overparameterized. Through experiments on U‑Nets trained on CelebA and a random‑features theoretical analysis, it shows that the interpolation peak occurs at a model size proportional to the product of training samples and noise realizations, but the test loss starts to rise already at the number of samples, leading to memorization of the empirical score. Regularization techniques such as ridge penalties or early stopping can still make large models outperform smaller, unregularized ones.

By Rapha\"el Urfin, Tony Bonnaire, Giulio Biroli, Marc M\'ezard
arXiv Machine Learning
Jul 7

Benign Overfitting Does Not Occur in Diffusion Models

arXiv:2607. 02671v1 Announce Type: cross Abstract: Benign overfitting and double descent have come to shape our understanding of generalization in deep learning, establishing that overfitting is not only compatible with good generalization but can actively benefit it.

By Tyler Farghly, Benjamin Dupuis, Alain Durmus, Umut Simsekli