Self-Supervised Learning with Data Augmentation Explained for Beginners

Self-supervised learning with data augmentation is a method where a computer program learns without needing many manual labels.

The program modifies existing data in various ways, for example by rotating, changing colors, or enlarging images.

These altered versions help the program recognize important features that do not change through simple modifications of the data.

This way, the program learns to better understand patterns and relationships in the data and can later solve new tasks more effectively.