GSURE-Based Diffusion Model Training with Corrupted Data
- URL: http://arxiv.org/abs/2305.13128v2
- Date: Thu, 13 Jun 2024 18:11:45 GMT
- Title: GSURE-Based Diffusion Model Training with Corrupted Data
- Authors: Bahjat Kawar, Noam Elata, Tomer Michaeli, Michael Elad,
- Abstract summary: We propose a novel training technique for generative diffusion models based only on corrupted data.
We demonstrate our technique on face images as well as Magnetic Resonance Imaging (MRI)
- Score: 35.56267114494076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more. However, training such models usually requires large amounts of clean signals which are often difficult or impossible to obtain. In this work, we propose a novel training technique for generative diffusion models based only on corrupted data. We introduce a loss function based on the Generalized Stein's Unbiased Risk Estimator (GSURE), and prove that under some conditions, it is equivalent to the training objective used in fully supervised diffusion models. We demonstrate our technique on face images as well as Magnetic Resonance Imaging (MRI), where the use of undersampled data significantly alleviates data collection costs. Our approach achieves generative performance comparable to its fully supervised counterpart without training on any clean signals. In addition, we deploy the resulting diffusion model in various downstream tasks beyond the degradation present in the training set, showcasing promising results.
Related papers
- Learning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method [9.173055778539641]
We propose a principled expectation-maximization (EM) framework that iteratively learns diffusion models from noisy data with arbitrary corruption types.
Our framework employs a plug-and-play Monte Carlo method to accurately estimate clean images from noisy measurements, followed by training the diffusion model using the reconstructed images.
arXiv Detail & Related papers (2024-10-15T03:54:59Z) - Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images [19.957503854446735]
Diffusion models (DMs) have emerged as powerful generative models for solving inverse problems.
FlowDiff is a joint training paradigm that leverages a conditional normalizing flow model to facilitate the training of diffusion models on corrupted data sources.
Our experiment shows that FlowDiff can effectively learn clean distributions across a wide range of corrupted data sources.
arXiv Detail & Related papers (2024-07-15T18:33:20Z) - Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection [9.784793380119806]
We introduce DIAG, a training-free Diffusion-based In-distribution Anomaly Generation pipeline for data augmentation.
Unlike conventional image generation techniques, we implement a human-in-the-loop pipeline, where domain experts provide multimodal guidance to the model.
We demonstrate the efficacy and versatility of DIAG with respect to state-of-the-art data augmentation approaches on the challenging KSDD2 dataset.
arXiv Detail & Related papers (2024-07-04T14:28:52Z) - An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations [21.411327264448058]
We propose an expectation-maximization (EM) approach to train diffusion models from corrupted observations.
Our method alternates between reconstructing clean images from corrupted data using a known diffusion model (E-step) and refining diffusion model weights based on these reconstructions (M-step)
This iterative process leads the learned diffusion model to gradually converge to the true clean data distribution.
arXiv Detail & Related papers (2024-07-01T07:00:17Z) - Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data [74.2507346810066]
Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data.
We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data.
arXiv Detail & Related papers (2024-03-20T14:22:12Z) - Ambient Diffusion Posterior Sampling: Solving Inverse Problems with
Diffusion Models trained on Corrupted Data [56.81246107125692]
Ambient Diffusion Posterior Sampling (A-DPS) is a generative model pre-trained on one type of corruption.
We show that A-DPS can sometimes outperform models trained on clean data for several image restoration tasks in both speed and performance.
We extend the Ambient Diffusion framework to train MRI models with access only to Fourier subsampled multi-coil MRI measurements.
arXiv Detail & Related papers (2024-03-13T17:28:20Z) - Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI)
In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion)
Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
arXiv Detail & Related papers (2024-02-15T18:59:18Z) - Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
We introduce a novel sampling framework called Steerable Conditional Diffusion.
This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.
We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
arXiv Detail & Related papers (2023-08-28T08:47:06Z) - Reflected Diffusion Models [93.26107023470979]
We present Reflected Diffusion Models, which reverse a reflected differential equation evolving on the support of the data.
Our approach learns the score function through a generalized score matching loss and extends key components of standard diffusion models.
arXiv Detail & Related papers (2023-04-10T17:54:38Z) - Negative Data Augmentation [127.28042046152954]
We show that negative data augmentation samples provide information on the support of the data distribution.
We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator.
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
arXiv Detail & Related papers (2021-02-09T20:28:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.