Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs
discriminator; GANs (generative adversarial nets); generator; image generation; pomegranate image; random noise
Image production has been transformed by generative adversarial networks (GANs), which have made unprecedented realism and diversity possible. Still, there are significant hurdles in managing datasetsdatasets managing and analyzing lossesloss analysis. This book chapter focusses on dataset administration and loss analysis, while providing a thorough method for using adversarial networks for image production. A thorough approach for selecting and preparing datasets, while maintaining optimal GAN performance is put forth by researchers. The proposed research approach enables the effective training of GANs, resulting in high-quality image generationhigh-quality image generation. Experimental results demonstrate the efficacy of the current method, showcasing improved image realism and diversity. The suggested strategy also presents a fresh way to examine discriminator and generator lossesgenerator losses, offering new perspectives on the convergence and stability of GANs. This study advances the field of GAN-based image productionGAN-based image production and offers professionals and academics who wish to use adversarial networks a priceless tool. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin.
Deepa, B.G.; Senthil, S.; Suhaas, K.P.; Poshith, L.; Sai Shashank, S.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1515/9783111354934-008 https://www.scopus.com/pages/publications/105038227414?origin=resultslist
Deepa B.G., Department of Computer Science, Christ University, Bengaluru, India; Senthil S., School of Computer Applications, Dayananda Sagar University, Bengaluru, India; Suhaas K.P., Department of ISE, The National Institute of Engineering, Mysore, India; Poshith L., School of CSA, REVA University, Bengaluru, India; Sai Shashank S., School of CSA, REVA University, Bengaluru, India