List of datasets in computer vision and image processing
Outline of machine learning
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In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure.[1] The goal of diffusion models is to learn a diffusion process that generates a probability distribution for a given dataset from which we can then sample new images. They learn the latent structure of a dataset by modeling the way in which data points diffuse through their latent space.[2]
In the case of computer vision, diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolution, and image generation. This typically involves training a neural network to sequentially denoise images blurred with Gaussian noise.[2][3] The model is trained to reverse the process of adding noise to an image. After training to convergence, it can be used for image generation by starting with an image composed of random noise for the network to iteratively denoise. Announced on 13 April 2022, OpenAI's text-to-image model DALL-E 2 is an example that uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image.[4] Diffusion models have recently found applications in natural language processing (NLP),[5] particularly in areas like text generation[6][7] and summarization.[8]
Diffusion models are typically formulated as Markov chains and trained using variational inference.[9] Examples of generic diffusion modeling frameworks used in computer vision are denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations.[10]
^Chang, Ziyi; Koulieris, George Alex; Shum, Hubert P. H. (2023). "On the Design Fundamentals of Diffusion Models: A Survey". arXiv:2306.04542 [cs.LG].
^ abSong, Yang; Sohl-Dickstein, Jascha; Kingma, Diederik P.; Kumar, Abhishek; Ermon, Stefano; Poole, Ben (2021-02-10). "Score-Based Generative Modeling through Stochastic Differential Equations". arXiv:2011.13456 [cs.LG].
^Cite error: The named reference dalle2 was invoked but never defined (see the help page).
^Li, Yifan; Zhou, Kun; Zhao, Wayne Xin; Wen, Ji-Rong (August 2023). "Diffusion Models for Non-autoregressive Text Generation: A Survey". Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization. pp. 6692–6701. arXiv:2303.06574. doi:10.24963/ijcai.2023/750. ISBN 978-1-956792-03-4.
^Han, Xiaochuang; Kumar, Sachin; Tsvetkov, Yulia (2023). "SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control". Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics: 11575–11596. arXiv:2210.17432. doi:10.18653/v1/2023.acl-long.647.
^Xu, Weijie; Hu, Wenxiang; Wu, Fanyou; Sengamedu, Srinivasan (2023). "DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM". Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA, USA: Association for Computational Linguistics: 9040–9057. arXiv:2310.15296. doi:10.18653/v1/2023.findings-emnlp.606.
^Zhang, Haopeng; Liu, Xiao; Zhang, Jiawei (2023). "DiffuSum: Generation Enhanced Extractive Summarization with Diffusion". Findings of the Association for Computational Linguistics: ACL 2023. Stroudsburg, PA, USA: Association for Computational Linguistics: 13089–13100. arXiv:2305.01735. doi:10.18653/v1/2023.findings-acl.828.
^Cite error: The named reference ho was invoked but never defined (see the help page).
^Croitoru, Florinel-Alin; Hondru, Vlad; Ionescu, Radu Tudor; Shah, Mubarak (2023). "Diffusion Models in Vision: A Survey". IEEE Transactions on Pattern Analysis and Machine Intelligence. 45 (9): 10850–10869. arXiv:2209.04747. doi:10.1109/TPAMI.2023.3261988. PMID 37030794. S2CID 252199918.
Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. It is considered to be a part of the ongoing...
The Bass model or Bass diffusionmodel was developed by Frank Bass. It consists of a simple differential equation that describes the process of how new...
spinodal decomposition. Diffusion is a stochastic process due to the inherent randomness of the diffusing entity and can be used to model many real-life stochastic...
Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett...
Facilitated diffusion (also known as facilitated transport or passive-mediated transport) is the process of spontaneous passive transport (as opposed...
diffusion tensor. The diffusion tensor model is a rather simple model of the diffusion process, assuming homogeneity and linearity of the diffusion within...
nitrogen. The concentration gradient, can be used as a model for the driving mechanism of diffusion. In this context, inert gas refers to a gas which is...
seen as a local volatility model d F t = v d W t {\displaystyle dF_{t}=v\,dW_{t}} . In the Bachelier model the diffusion coefficient is a constant v...
models have acknowledged that neither of these models accounts adequately for the available experimental data. This model, a reaction–diffusionmodel...
Demic diffusion, as opposed to trans-cultural diffusion, is a demographic term referring to a migratory model, developed by Luigi Luca Cavalli-Sforza...
In cultural anthropology and cultural geography, cultural diffusion, as conceptualized by Leo Frobenius in his 1897/98 publication Der westafrikanische...
custom version of the source-available Stable Diffusion text-to-image diffusionmodel called NovelAI Diffusion, which is trained on a Danbooru-based dataset...
addition to simple diffusion. These models can be applied to limb formation and teeth development among other examples. Reaction-diffusionmodels can be used...
the gradient are free diffusion, restricted diffusion, transcytosis, and cytoneme-assisted transport. The free diffusionmodel assumes Dpp to diffuse...
fine-tuning of Stable Diffusion, an existing open-source model for generating images from text prompts, on spectrograms. This results in a model which uses text...
to model many real-life stochastic systems. Brownian motion, reflected Brownian motion and Ornstein–Uhlenbeck processes are examples of diffusion processes...
pattern formation in biological systems include the classical reaction–diffusionmodel proposed by Alan Turing and the more recently found elastic instability...
acclaimed 1962 book Diffusion of Innovations (now in its fifth edition). Bass diffusionmodelDiffusion (business) Hype cycle Lazy user model Matching person...
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