מעבר מהעתקת דגימות: הזיכרון המבני בדגמי התפשטות

תקציר מקורי באנגליתarXiv:2603.13419v3 Announce Type: replace Abstract: Diffusion models generalize well in practice. Paradoxically, an optimal diffusion model fully memorizes the training data and therefore fails to generalize, raising the question of what induces generalization in a real diffusion model. We show that diffusion models progressively overfit the denoising training objective, creating a generalization gap between validation and training performance at intermediate noise levels. In a fully analytic 2D toy model with a controlled denoising error, we trace this gap to the interaction between model error and the density of the data distribution's support. The optimal denoising flow field localizes sharply around individual training points, whereas model error suppresses exact recall of training poi