benediktfesl/Diffusion_MSE
Implementation of the paper "On the Asymptotic Mean Square Error Optimality of Diffusion Models."
This project provides an implementation to reproduce research results on the asymptotic mean square error optimality of diffusion models, especially for denoising tasks. It takes various data types like image data (e.g., MNIST, Fashion MNIST) or audio data, applies denoising using diffusion models or Gaussian Mixture Model (GMM) baselines, and outputs evaluation metrics. This tool is designed for researchers in machine learning or signal processing who are investigating the theoretical properties and performance of diffusion models for denoising.
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Use this if you are a researcher who needs to reproduce or validate the simulation results related to the asymptotic mean square error optimality of diffusion models for denoising tasks.
Not ideal if you are looking for a user-friendly application to simply apply denoising to your own custom images or audio without a research focus on model optimality.
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14
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1
Language
Python
License
BSD-3-Clause
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Last pushed
May 14, 2025
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