30 citations · 106 across the 22 of their papers we have counts for
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Adversarial Learning of Classifier-Free Guidance Schedules
Ashwini Pokle, Alexandre Galashov, Arnaud Doucet +2
Modern text-to-image diffusion models rely on classifier-free guidance (CFG) to achieve high image fidelity and text alignment. However, CFG typically applies a static, global scal…
The Geometry of Noise: Why Diffusion Models Don't Need Noise Conditioning
Mojtaba Sahraee-Ardakan, Mauricio Delbracio, Peyman Milanfar
Autonomous (noise-agnostic) generative models, such as Equilibrium Matching and blind diffusion, challenge the standard paradigm by learning a single, time-invariant vector field t…
Learn to Guide Your Diffusion Model
Alexandre Galashov, Ashwini Pokle, Arnaud Doucet +3
Classifier-free guidance (CFG) is a widely used technique for improving the perceptual quality of samples from conditional diffusion models. It operates by linearly combining condi…
On the Relation Between Linear Diffusion and Power Iteration
Dana Weitzner, Mauricio Delbracio, Peyman Milanfar +1
Recently, diffusion models have gained popularity due to their impressive generative abilities. These models learn the implicit distribution given by the training dataset, and samp…
A Survey on Diffusion Models for Inverse Problems
Giannis Daras, Hyungjin Chung, Chieh-Hsin Lai +5
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples. This has unlocked exciting new possibilities for so…
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
Peyman Milanfar, Mauricio Delbracio
Denoising, the process of reducing random fluctuations in a signal to emphasize essential patterns, has been a fundamental problem of interest since the dawn of modern scientific i…