Comparative Analysis of Self-Supervised Learning and Supervised Learning for Large-Scale AI Models
Abstract
The growing need for labeled data in supervised learning has led to the rise of self-supervised learning (SSL) techniques. This paper compares the performance of SSL-based models, such as SimCLR and BYOL, with traditional supervised deep learning methods in various domains, including image recognition, speech processing, and anomaly detection. Our evaluation examines trade-offs in data efficiency, generalization capability, and computational cost, demonstrating the advantages of SSL in reducing dependency on labeled datasets while maintaining high model performance.
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