Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies

Title

Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies

Creator

Deepa, S.; Sheetal; Alli, A.; Siddalingappa, Rashmi

Description

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.

Source

SSRG International Journal of Electronics and Communication Engineering;Volume;12;Issue;7;pp.90-101

Date

01-01-2025

Publisher

Seventh Sense Research Group

Subject

Computer Vision; Contrastive Clustering; Hybrid SSL; Image classification; Multi-Modal; Self-Supervised

Coverage

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

Rights

All Open Access; Gold Open Access

Relation

ISSN: 23488549;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.14445/23488549/IJECE-V12I7P108

https://www.scopus.com/pages/publications/105012894771?origin=resultslist