Harnessing Stochasticity: Diffusion Models as a Paradigm for Generative Modeling in Deep Learning
Sundeep Goud Katta
, Peda Venki Pola
Harnessing Stochasticity: Diffusion Models as a Paradigm for Generative Modeling in Deep Learning
Diffusion models have recently gained prominence in the field of deep learning as a powerful class of generative models. By modeling the data generation process as a stochastic diffusion process, these models address several limitations of traditional generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). This article provides a comprehensive overview of diffusion models, their theoretical foundations, and technical implementations. We delve into the specifics of Denoising Diffusion Probabilistic Models (DDPMs), explore their advancements, and discuss their applications across various domains.
"Harnessing Stochasticity: Diffusion Models as a Paradigm for Generative Modeling in Deep Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.a473-a482, October-2024, Available :https://ijnrd.org/papers/IJNRD2410050.pdf
Volume 9
Issue 10,
October-2024
Pages : a473-a482
Paper Reg. ID: IJNRD_300896
Published Paper Id: IJNRD2410050
Downloads: 00032
Research Area: Science and Technology
Country: -, -, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
