Transfer learning with self-monitoring is primarily applied in the fields of artificial intelligence, automation, and Industry 4.0. It is an intelligent method whereby machines or computer systems transfer previously acquired knowledge to new tasks. The special feature of this is that the systems monitor themselves in order to control the quality of their learning processes and detect errors early on.<\/p>\n
Imagine a robot in a factory has learned to sort screws. Now it's tasked with sorting nails without having to learn from scratch. Using self-supervised transfer learning, the robot leverages its existing knowledge of identifying small metal parts and independently adapts it to the new task. Through self-supervision, the robot also checks if its results are reliable and immediately reports if there are any problems.<\/p>\n
This saves time and costs, and increases quality because less human intervention is needed. This allows companies to react more flexibly to new requirements while simultaneously increasing the efficiency of their processes. Transfer learning with self-supervision is therefore an important building block for the intelligent automation of the future.<\/p>","protected":false},"excerpt":{"rendered":"