Supervised contrastive learning is a term from the fields of artificial intelligence, big data and smart data, and digital transformation. It describes a method by which computers learn to recognise differences and similarities between data using examples.

This way, the computer is not only shown individual data points, but always pairs of similar and dissimilar examples. For instance, the system is shown a picture of a cat and another cat – these are meant to be recognised as „similar“. Alongside these, a picture of a dog is also shown – this picture is meant to be recognised as „dissimilar“ to the cats. Through these comparisons, artificial intelligence can increasingly learn how different things are distinguished from each other and what they have in common.

Supervised contrastive learning is used whenever particularly reliable distinctions are needed – for example, for fraud detection in bank transactions or for facial recognition in photos. Because clear pairs with similarities and differences are provided, the AI learns faster and more accurately than with conventional methods.

For companies, this means: this technology can help to recognise patterns more quickly, make predictions more accurately, and thus optimise everyday processes.

Supervised contrastive learning is a term from the fields of artificial intelligence, big data and smart data, and digital transformation. It describes a method by which computers learn to recognise differences and similarities between data using examples.

This way, the computer is not only shown individual data points, but always pairs of similar and dissimilar examples. For instance, the system is shown a picture of a cat and another cat – these are meant to be recognised as „similar“. Alongside these, a picture of a dog is also shown – this picture is meant to be recognised as „dissimilar“ to the cats. Through these comparisons, artificial intelligence can increasingly learn how different things are distinguished from each other and what they have in common.

Supervised contrastive learning is used whenever particularly reliable distinctions are needed – for example, for fraud detection in bank transactions or for facial recognition in photos. Because clear pairs with similarities and differences are provided, the AI learns faster and more accurately than with conventional methods.

For companies, this means: this technology can help to recognise patterns more quickly, make predictions more accurately, and thus optimise everyday processes.