Self-Supervised Learning (SSL): Enhancing Few-Shot Image Classification with Limited Labeled Data Exploration


  • K. Nirmaladevi Assistant Professor, Department of Computer Science and Engineering, Panimalar Engineering College, Chennai
  • K. Revathi Research Scholar & Assistant Professor, Department of Information Technology, SNS College of Engineering, Coimbatore
  • K.B. Kishore Mohan Professor & Head, Department of Bio Medical Engineering, Sri Shanmugha College of Engineering and Technology - [SSCET], Sankari, Salem
  • J. Jayapradha Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India
  • T. Senthil Kumar Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India


Self-supervised learning, Few-shot image classification, Labeled data scarcity, feature extraction, contrastive learning


This research investigates the application of self-supervised learning techniques to enhance few-shot image classification in scenarios with limited labeled data. Traditional supervised learning approaches often struggle in settings where annotated examples are scarce. The study focuses on developing strategies to augment the effectiveness of few-shot image classification models when confronted with a shortage of labeled training samples. The proposed approach involves employing self-supervised learning (SSL) methods to uncover latent patterns and representations within the unlabeled data, allowing the model to generalize more effectively to new classes with minimal labeled instances. Various self-supervised learning strategies, including contrastive learning and temporal consistency, are examined to enhance feature extraction and classification performance. Through experimentation on CIFAR-100 datasets, it is demonstrated that the self-supervised learning framework significantly improves few-shot image classification accuracy compared to traditional supervised approaches. Furthermore, the implications of the findings for real-world applications, where acquiring labeled data is resource-intensive or impractical, are discussed. This research contributes valuable insights into the synergy between self-supervised learning and few-shot image classification, offering a promising avenue for addressing data scarcity challenges in image recognition tasks.


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How to Cite

Nirmaladevi, K. ., Revathi, K. ., Kishore Mohan, K. ., Jayapradha, J. ., & Kumar, T. S. . (2024). Self-Supervised Learning (SSL): Enhancing Few-Shot Image Classification with Limited Labeled Data Exploration. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 437 –. Retrieved from



Research Article