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 Conference Paper Faculty Publications- Conference Papers 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 Swetha, S.; Prakash, Gladis Sam; Thomas, Jestin; Kalpana, P.; Jose, Teena; Sumathi, P. Title A name given to the resource Synthetic Data Augmentation for Robust Solar Flare Classification: A Comparative Analysis of Conditional DCGAN, VAE, and Diffusion Models 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 Proceedings of 2nd International Conference on Visual Analytics and Data Visualization, ICVADV 2026;pp.896-901 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICVADV67766.2026.11470543" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICVADV67766.2026.11470543</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037458658?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037458658?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 Swetha S., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Prakash G.S., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Thomas J., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Kalpana P., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Jose T., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Sumathi P., Vyasa College, Department of Computer Science, Tamil Nadu, Salem, India Description An account of the resource Solar flares are extremely dangerous to the ground and space-based resources. Solar flares have to be classified properly and in good time to offer protection to assets in both the environments. Deep Learning-based flares have been divided into 3 classes (C, M and X). The main problems with this kind of classification are that high impact M- or X-class solar flares are extremely rare, and cannot be well sampled, thus leading to a very unbalanced sample. This paper exemplifies a comparative analysis of three models of Conditional Generative Models (cDCGAN models, cVAE models and cDDPM) to produce realistic images of solar flares considering each of the low frequency and high-impact solar flare types. The research question will be how such models can be evaluated in terms of their capacity to create realistic, class-specific images (magnetograms, and EUV) and time-series data which could be used to create class-balanced training samples. The initial experiments make use of the cDCGAN, cVAE, cDDPM architecture and considering the generation of class-conditional solar flare images portray high levels of stability (convergence was stated in less than 600 epochs in the case of the cDCGAN, cVAE models and cDDPM) and the generation of images which could be considered as practically indistinguishable to real life images. The results indicate that cDDPM may be a possible solution to a high-fidelity production of solar features. We measured the efficiency of these models in quantitative terms (popular metrics, like the Frhet Inception Distance, Structural Similarity Index) in a manner that we could determine the best manner of training model based solar flare classification systems using realistic data. This research is aligned with Sustainability Development Goals 9- Industry, Innovation and Infrastructure with focus on verticals 9.1 and 9.5. 2026 IEEE. Subject The topic of the resource cDCGAN; Class Imbalance; Conditional Diffusion Model; Conditional Generative Models; cVAE; Data Augmentation; SDG Goal 9; Solar Flare Classification; Synthetic Data Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159237-0; Language A language of the resource English Type The nature or genre of the resource Conference paper 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