preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202408.0316.v1 (registering DOI)

Preprint Review Version 1 This version is not peer-reviewed

Version 1 : Received: 5 August 2024 / Approved: 5 August 2024 / Online: 6 August 2024 (08:36:05 CEST)

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As DMs are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of DMs, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of DMs and provide guidelines for future researchers and practitioners across diverse disciplines.

Diffusion Models; Generative Modeling; Synthetic Data Generation; Image Synthesis; Image-to-Image Translation; Text-to-Image Generation; Audio Synthesis; Time Series Forecasting; Anomaly Detection

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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