Related papers: An attempt to generate new bridge types from latent space of denoising diffusion Implicit model

An attempt to generate new bridge types from latent space of denoising diffusion Implicit model

URL: http://arxiv.org/abs/2402.07129v1
Date: Sun, 11 Feb 2024 08:54:37 GMT
Title: An attempt to generate new bridge types from latent space of denoising diffusion Implicit model
Authors: Hongjun Zhang
Abstract summary: Use denoising diffusion implicit model for bridge-type innovation. Process of adding noise and denoising to an image can be likened to the process of a corpse rotting and a detective restoring the scene of a victim being killed, to help beginners understand.
Score: 2.05750372679553
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Use denoising diffusion implicit model for bridge-type innovation. The process of adding noise and denoising to an image can be likened to the process of a corpse rotting and a detective restoring the scene of a victim being killed, to help beginners understand. Through an easy-to-understand algebraic method, derive the function formulas for adding noise and denoising, making it easier for beginners to master the mathematical principles of the model. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge , based on Python programming language, TensorFlow and Keras deep learning platform framework , denoising diffusion implicit model is constructed and trained. From the latent space sampling, new bridge types with asymmetric structures can be generated. Denoising diffusion implicit model can organically combine different structural components on the basis of human original bridge types, and create new bridge types.

Related papers

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns [33.293193191683145]
We present a single generative model which can learn to generate multiple types of noise as well as blend between them. We also present an application of our model to improving inverse procedural material design.
arXiv Detail & Related papers (2024-04-25T02:23:11Z)
Blue noise for diffusion models [50.99852321110366]
We introduce a novel and general class of diffusion models taking correlated noise within and across images into account. Our framework allows introducing correlation across images within a single mini-batch to improve gradient flow. We perform both qualitative and quantitative evaluations on a variety of datasets using our method.
arXiv Detail & Related papers (2024-02-07T14:59:25Z)
An attempt to generate new bridge types from latent space of PixelCNN [2.05750372679553]
PixelCNN can capture the statistical structure of the images and calculate the probability distribution of the next pixel. From the obtained latent space sampling, new bridge types different from the training dataset can be generated.
arXiv Detail & Related papers (2024-01-11T15:06:25Z)
An attempt to generate new bridge types from latent space of generative adversarial network [2.05750372679553]
Symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge are used. Based on Python programming language, and Keras deep learning platform framework, generative adversarial network is constructed and trained.
arXiv Detail & Related papers (2024-01-01T08:46:29Z)
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge [13.610545398309464]
We introduce the Generalized Ornstein-Uhlenbeck Bridge (GOUB) model. By leveraging the natural mean-reverting property of the generalized OU process, we achieve diffusion mappings from point to point. We also present the corresponding Mean-ODE model adept at capturing both pixel-level details and structural perceptions.
arXiv Detail & Related papers (2023-12-16T03:09:28Z)
Denoising Diffusion Bridge Models [54.87947768074036]
Diffusion models are powerful generative models that map noise to data using processes. For many applications such as image editing, the model input comes from a distribution that is not random noise. In our work, we propose Denoising Diffusion Bridge Models (DDBMs)
arXiv Detail & Related papers (2023-09-29T03:24:24Z)
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement [87.08496758469835]
This work addresses the issue by seamlessly integrating a diffusion model with a physics-based exposure model. Our method obtains significantly improved performance and reduced inference time compared with vanilla diffusion models. The proposed framework can work with both real-paired datasets, SOTA noise models, and different backbone networks.
arXiv Detail & Related papers (2023-07-15T04:48:35Z)
OCD: Learning to Overfit with Conditional Diffusion Models [95.1828574518325]
We present a dynamic model in which the weights are conditioned on an input sample x. We learn to match those weights that would be obtained by finetuning a base model on x and its label y.
arXiv Detail & Related papers (2022-10-02T09:42:47Z)
Dynamic Dual-Output Diffusion Models [100.32273175423146]
Iterative denoising-based generation has been shown to be comparable in quality to other classes of generative models. A major drawback of this method is that it requires hundreds of iterations to produce a competitive result. Recent works have proposed solutions that allow for faster generation with fewer iterations, but the image quality gradually deteriorates.
arXiv Detail & Related papers (2022-03-08T11:20:40Z)
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
arXiv Detail & Related papers (2021-01-08T02:03:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.