Contrastive self-supervision is a method where computers learn to recognize important features in data without being told exactly what to look for.
This works by the system comparing different versions of the same information and learning to recognize them as similar while treating other information as different.
This way, the machine can discover important patterns, for example in images or texts, and thereby better prepare for new tasks.
This type of learning is especially useful when there are few or no labeled examples.