Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Deepa, B.G.; Senthil, S.; Suhaas, K.P.; Poshith, L.; Sai Shashank, S. Title A name given to the resource Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Quantum Computing;Volume;5;pp.137-161 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/9783111354934-008" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/9783111354934-008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038227414?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038227414?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource discriminator; GANs (generative adversarial nets); generator; image generation; pomegranate image; random noise Publisher An entity responsible for making the resource available Walter de Gruyter GmbH Relation A related resource ISSN: 29400112; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online