What Is Self-Supervised Learning? How Models Learn from Unlabeled Data

Self-supervised learning enables AI models to learn from unlabeled data by creating training targets from the data's own structure, allowing large-scale representation learning without manual labels. This approach involves a defined input, a characteristic transformation, and an evaluable outcome, distinguishing it from unsupervised learning without explicit objectives.