A Machine Learning Framework for Generating Photorealistic Photos of Real Time Objects using Adam Optimizer by a Generative Adversarial Network (GAN)

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Learning (Supervised)
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Published: Dec 31, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i12.5888
Keywords:
Framework, Objects, Machine Learning, Adversarial Networks

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Lakshmikanth Paleti
Associate Professor, Department of CSBS, R.V.R & J.C College of Engineering, Chowdavaram, A.P, India.
Ramakrishna Badiguntla
Assistant Professor, Department of CSE(DS), R.V.R & J.C College of Engineering, Chowdavaram, A.P, India.
H. Venkateshwara Reddy
Professor, CSE Department, Vardhaman college of engineering, Hyderabad, India.
K. Prabhakar
Associate Professor, Dept. of CSE, school of technology, gitam deemed to be university, Visakhapatnam, AP.
CH. Suresh Babu
Associate Professor, IT Department, SR Gudlavalleru Engineering college, Andhra Pradesh, India.
K. Vamsi Krishna
Project Manager, Prowess software services, Hyderabad, India

Abstract

Photographic training can result in new photographs that, to human observers, appear to be at least superficially authentic, with many realistic features. will discuss a number of intriguing GAN applications in order to help you develop an understanding of the types of problems where GANs can be used and useful. It is not an exhaustive list, but it includes numerous examples of GAN applications that have garnered media attention. This Paper Proposes a Framework for Generating Photorealistic Photos of real time objects (FGPPO) using Adam Optimizer by Generative Adversarial Networks.

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How to Cite
Paleti, L., Badiguntla, R., Reddy, H. V., Prabhakar, K. ., Babu, C. S., & Krishna, K. V. (2022). A Machine Learning Framework for Generating Photorealistic Photos of Real Time Objects using Adam Optimizer by a Generative Adversarial Network (GAN). International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 76–82. https://doi.org/10.17762/ijritcc.v10i12.5888
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Vol. 10 No. 12 (2022): December (2022) Issue
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Articles

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