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 Article Faculty Publications -Articles 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, S.; Sheetal; Alli, A.; Siddalingappa, Rashmi Title A name given to the resource Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived SSRG International Journal of Electronics and Communication Engineering;Volume;12;Issue;7;pp.90-101 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/23488549/IJECE-V12I7P108" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/23488549/IJECE-V12I7P108</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012894771?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012894771?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 S., Department of Computer Science, Christ University, Bengaluru, India; Sheetal, Department of Computer Applications, Presidency College, Bengaluru, India; Alli A., Department of Computer Applications, Presidency College, Bengaluru, India; Siddalingappa R., Department of Computer Science, Christ University, Bengaluru, India Description An account of the resource Image classification is a cornerstone of computer vision, with the applications spanning healthcare, autonomous driving and security. The dependence on large labeled datasets for supervised learning poses significant challenges, particularly in specialized fields where the labeled data is scarce and expensive to obtain. Self-supervised learning (SSL) has emerged as a promising paradigm, enabling models to learn useful representations from unlabelled data by designing pretext tasks that generate pseudo-labels. SSL faces limitations in handling complex data distributions and achieving robust generalization. This paper explores hybrid self-supervised learning strategies that combine multiple SSL techniques, such as contrastive learning, masked image modeling, and clustering, to enhance image classification performance and reduce dependence on labeled data. This study proposes a comprehensive framework that integrates data augmentation, feature extraction, and hybrid learning mechanisms, evaluated on the CIFAR-100 dataset. The experimental results demonstrate that hybrid SSL approaches achieve significant improvements in performance. The combination of SimCLR and masked image modeling (MAE) achieves a Top-1 accuracy of 77.8% on the clean test set and 71.4% on the domain-shifted set, and self-distillation with contrastive learning (DINO) achieves the highest Top-1 accuracy of 78.4% on the clean test set and 72.1% on the domain-shifted set. Advanced data augmentation techniques, such as CutMix and RandAugment, additionally enhance model robustness, with SwAV (contrastive clustering) achieving 76.5% Top-1 accuracy on the clean test set and 70.1% on the domain-shifted set. The findings highlight the effectiveness of hybrid SSL methods in addressing the challenges of limited labelled data, offering valuable insights for future research and applications in image classification. 2025 Seventh Sense Research Group. Subject The topic of the resource Computer Vision; Contrastive Clustering; Hybrid SSL; Image classification; Multi-Modal; Self-Supervised Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23488549; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online