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GEOPHYS: The Geometry of Physical Plausibility
Christian Internò, Alexander Pondaven, Habon Issa +8
While humans can identify physically implausible events within milliseconds, machine learning approaches addressing the same problem are extremely slow and expensive. They either r…
Learning a Maximum Entropy Model for Visual Textures using Diffusion
Xinyuan Zhao, Eero P. Simoncelli
Visual textures -- spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree) -- are ubiquitous in visual scenes and provide impor…
Unconditional CNN denoisers contain sparse semantic representation of images
Zahra Kadkhodaie, Stéphane Mallat, Eero Simoncelli
Generative diffusion models learn probability densities over diverse image datasets by estimating the score with a neural network trained to remove noise. Despite their remarkable…
Learning predictable and robust neural representations by straightening image sequences
Xueyan Niu, Cristina Savin, Eero P. Simoncelli
Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate…
Video prediction using score-based conditional density estimation
Pierre-Étienne H. Fiquet, Eero P. Simoncelli
Temporal prediction is inherently uncertain, but representing the ambiguity in natural image sequences is a challenging high-dimensional probabilistic inference problem. For natura…
Feature-guided score diffusion for sampling conditional densities
Zahra Kadkhodaie, Stéphane Mallat, Eero P. Simoncelli
Score diffusion methods can learn probability densities from samples. The score of the noise-corrupted density is estimated using a deep neural network, which is then used to itera…
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
Ling-Qi Zhang, Zahra Kadkhodaie, Eero P. Simoncelli +1
We examine the problem of selecting a small set of linear measurements for reconstructing high-dimensional signals. Well-established methods for optimizing such measurements includ…
Generalization in diffusion models arises from geometry-adaptive harmonic representations
Zahra Kadkhodaie, Florentin Guth, Eero P. Simoncelli +1
Deep neural networks (DNNs) trained for image denoising are able to generate high-quality samples with score-based reverse diffusion algorithms. These impressive capabilities seem…