Self-supervised learning with masking is a technique where a computer learns to guess missing parts of data without needing many examples with explanations.
Imagine you have a text or an image where words or areas are randomly covered, and you try to fill in these missing parts.
The computer does something similar: it learns to understand from the context what might be in the covered spots.
This way, it can recognize important patterns and structures in the data, even when no direct labels are present.
This method is often used to make computers better and more flexible in language processing or image recognition.