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Found 12 papers, 9 papers with code
Nested Diffusion Processes for Anytime Image Generation
Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising steps.
GSURE-Based Diffusion Model Training with Corrupted Data
Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more.
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e. g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e. g., "a pack of blue roses").
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --
Our aim is to give a better context to recent discoveries, and to the influence of DL in our domain.
Imagic: Text-Based Real Image Editing with Diffusion Models
In this paper we demonstrate, for the very first time, the ability to apply complex (e. g., non-rigid) text-guided semantic edits to a single real image.
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inverse problems.
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance
In order to obtain class-conditional generation, it was suggested to guide the diffusion process by gradients from a time-dependent classifier.
Do Perceptually Aligned Gradients Imply Adversarial Robustness?
In this work, we focus on this trait and test whether \emph{Perceptually Aligned Gradients imply Robustness}.
Threat Model-Agnostic Adversarial Defense using Diffusion Models
Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks.
Denoising Diffusion Restoration Models
Many interesting tasks in image restoration can be cast as linear inverse problems.
SNIPS: Solving Noisy Inverse Problems Stochastically
In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples from the posterior distribution of any linear inverse problem, where the observation is assumed to be contaminated by additive white Gaussian noise.
Stochastic Image Denoising by Sampling from the Posterior Distribution
Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image.
