Self-Supervised Learning explained for beginners

Self-Supervised Learning is a method where computers learn to recognize patterns in data without someone showing them the correct answers.

You can imagine it like putting together a puzzle without seeing the picture on the box – the computer uses parts of the data to predict other parts.

This way, the machine learns from the data itself without needing many labeled examples.

This helps to gain knowledge from large amounts of data, even when no precise explanations or labels are available.

Self-Supervised Learning is often used with images, texts, or speech to train better models.