Hugging Face Trending Papers
Valid Early Detection of Memorization in Diffusion Models
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv Statistics ML
4d ago
Valid Early Detection of Memorization in Diffusion Models
arXiv:2610.34617v1 Announce Type: cross Abstract: Diffusion models can sometimes reproduce examples from their training data, a phenomenon known as memorization. Existing memorization detection metho...
arXiv Machine Learning
Oct 9
Early Signatures of Memorization in Diffusion Models via Basin Geometry and Cyclic Denoising
The paper demonstrates that diffusion models encode memorization in the geometry of their learned energy landscape before it becomes visible in generated samples. By analyzing score divergence and basin volume, the authors show that localized basins form around training images early in training, and that cyclic denoising can recover these images even when one‑shot generation shows no copies. Experiments on Gaussian mixtures, CelebA, CIFAR‑10, and Stable Diffusion v1.4 confirm that basin geometry reveals memorization across various settings.
arXiv Computer Vision
Sep 2
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
arXiv:2609.00955v1 Announce Type: new Abstract: Diffusion models achieve strong image generation quality but incur high iterative denoising costs. Analog compute-in-memory (CIM) can accelerate matrix...
arXiv Machine Learning
Oct 9
Beyond Sample Copying: Structural Memorization in Diffusion Models
The paper investigates why diffusion models, despite their practical success, can fully memorize training data when optimal. It shows that as training proceeds, the denoising objective causes overfitting at intermediate noise levels, creating a gap between validation and training performance. In a 2D toy model, this gap arises from the interplay between model error and the data distribution’s support density, leading to a smooth, generalizing flow field rather than exact recall of training points. The study also reveals that predictions from intermediate inference states become increasingly similar to training data in feature space, even as overall similarity to validation data remains unchanged.
arXiv AI
Jul 1
Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning
arXiv:2510. 17917v2 Announce Type: replace-cross Abstract: Data unlearning aims to remove the influence of specific training samples from a trained model.
arXiv Machine Learning
Oct 15
Loss Curvature as a Sampling-Free Proxy for Memorization in Diffusion Models
arXiv:2610.19042v1 Announce Type: new Abstract: Understanding the mechanism behind why diffusion models memorize their training data remains an open question in generative modeling. In this paper, we...