What is Self-Supervised Learning? Understanding the Core Concept and Its Advantages
Self-supervised learning is an emerging technique in the field of machine learning that is gaining significant attention. Unlike traditional supervised learning, where models are trained on labeled data, self-supervised learning (SSL) involves training models on data without explicit labels. Instead, it generates its own “supervision” by predicting part of the input data using the remaining parts. This method has shown great promise in solving tasks where labeled data is scarce or difficult to obtain. Let’s delve deeper into the core concept of self-supervised learning and explore its advantages.
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