International Journal of Scientific Research and Engineering Development

The use of massive labeled data to train machine learning models has been a major limiting factor to the brisk pace of machine learning development, as large-scale datasets are both costly, labor-intensive, and timeconsuming to acquire. This is especially a major limitation when dealing with large scale areas of application, like in healthcare, autonomous systems, and industrial monitoring, where labeled data can be very limited, sensitive or expensive to maintain. To address this dependency, self-supervised learning (SSL) has become a groundbreaking method that can be used to acquire informative and robust representations on unlabeled data. SSL uses pretext tasks, contrastive learning and generative reconstruction algorithms to learn the underlying structures and semantic features without direct supervision and therefore does not require large labeled datasets. The present paper introduces a wide survey of the state-of-the-art methods of the SSL, its architectures, training methods, and its performance in different areas of application, such as computer vision, natural language processing, and healthcare informatics. We offer a comprehensive discussion of the strengths, weaknesses, and trade-offs of the various paradigms of the SSL, in terms of their capacity to enhance label efficiency, model generalization, and scalability in the real world. In addition, we address the idea of combining downstream tasks with SSL and its prospects in the resource-constrained world and ways to make it more robust to noise and domain changes. Lastly, we determine the open research issues and suggest future ways forward on the development of theSSL methodologies with a focus on their role in democratizing AI as it would minimize the labeled data requirements whilst preserving high performance. The knowledge acquired in this paper will help researchers and practitioners to use the power of SSL to design scalable, effective, and robust machine learning systems with limited support of labelled data.

Muhammad Faheem, Arbaz Haider Khan, Ahmad Yousaf Gill, Aqib Iqbal,"Self-Supervised Learning Techniques for Reducing Labeled Data Dependency" International Journal of Scientific Research and Engineering Development, V9(1): Page(785-795) Jan-Feb 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.