Self-Supervised Learning: Training AI Models with Less Labels

Artificial intelligence has come a long way, yet most models still depend heavily on labeled data — a scarce and expensive resource. In many real-world scenarios, gathering enough annotated examples is impractical or even impossible. But what if machines could learn directly from raw, unlabeled data, just as humans often do by observing and predicting patterns in the world around them? That is the promise of self-supervised learning. It allows models to generate their own supervision from data itself, discovering meaningful representations without the need for manual labels. In this webinar, we will explore what self-supervised learning is, why it has become one of the most important learning schemes in modern AI. We will unpack the core methods driving this paradigm, including contrastive learning and self-predictive learning. You will also discover what is possible with Self-Supervised Learning from an applied perspective.