Self-Supervised Learning for Low-Resource Environments Reducing Dependency on Labeled Data
Keywords:
Self-supervised learning, low-resource environments, unlabeled data, data efficiency, representation learningAbstract
Self-supervised learning (SSL) has emerged as a transformative paradigm within Machine Learning, addressing the critical challenge of labeled data scarcity in low-resource environments. Traditional supervised approaches rely heavily on extensive annotated datasets, which are often expensive, time-consuming, and impractical to obtain in domains such as low-resource languages, rural healthcare, and developing regions. This study explores how Self-Supervised Learning leverages large volumes of unlabeled data through pretext tasks to learn meaningful representations that can be effectively transferred to downstream tasks. By analyzing recent models and experimental findings, the paper demonstrates that SSL significantly reduces dependency on labeled data while maintaining competitive performance levels. Furthermore, it highlights improvements in data efficiency, scalability, and adaptability under constrained computational settings. The study concludes that SSL provides a viable and sustainable solution for democratizing artificial intelligence, enabling broader accessibility and practical deployment in resource-limited contexts.
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