Published 04-12-2023
Keywords
- Deep Learning,
- Self-Supervised Learning
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Self-supervised learning (SSL) has emerged as a revolutionary approach within the realm of deep learning, enabling models to learn effective representations from unlabeled data. This paper investigates the application of deep learning models in self-supervised learning, emphasizing their ability to leverage large quantities of unlabeled data to enhance representation learning in various domains, such as computer vision and natural language processing. By utilizing innovative training strategies that exploit the inherent structure of unlabeled datasets, SSL methods have demonstrated substantial improvements in performance on downstream tasks, often rivaling or surpassing their fully supervised counterparts. This study discusses key self-supervised learning techniques, their implementations, challenges, and future directions, highlighting the potential of SSL to reshape the landscape of machine learning, particularly in situations where labeled data is scarce.
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