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Self-supervised learning (SSL) represents a significant shift in the field of artificial intelligence (AI), addressing the challenge of leveraging vast amounts of unlabeled data. Unlike traditional supervised learning that relies heavily on labeled datasets, SSL enables models to generate their own labels from the data itself through pretext tasks. This paradigm has shown remarkable success in various domains such as natural language processing, computer vision, and healthcare. By learning useful representations without the need for extensive labeled data, SSL not only reduces the cost and effort associated with data annotation but also enhances model generalization and performance. This article explores the fundamentals of self-supervised learning, its recent advancements, and its applications across different sectors. We also discuss the challenges and limitations of SSL, and the potential it holds for the future of AI. Keywords: Self-Supervised Learning, Artificial Intelligence, Unlabeled Data, Natural Language Processing, Computer Vision, Healthcare, Representation Learning, Machine Learning, Pretext Tasks, Model Generalization
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