Robust transfer learning (Glossary)

The term "robust transfer learning" originates from artificial intelligence and also plays an important role in automation, industry and Factory 4.0.

Robust transfer learning describes a method in which an already trained AI model transfers its knowledge to a new but similar task - and is particularly resistant to changes or disruptions. The aim is for machines not to have to learn from scratch every time, but to utilise existing knowledge safely and reliably.

A simple example: an AI has been trained to recognise defective parts on a production line. With Robust Transfer Learning, this AI can effectively apply the knowledge it has learnt even if the light in the factory changes or new components are added. The model remains accurate and efficient even when it encounters new situations.

Robust transfer learning helps companies to implement AI solutions faster and more cost-effectively, as there is no need for time-consuming retraining. In industry and automation in particular, this ensures more flexible, reliable processes and protects against unexpected failures.