The term knowledge transfer between models originates from the fields of artificial intelligence, Big Data and Smart Data, and automation. It concerns how an already trained AI model can pass on its knowledge to another model, allowing that model to learn faster and more efficiently.
Imagine that an AI model has already learned to recognise cats in images very well. Instead of training a second model from scratch, the experience of the first model is utilised. This saves time, energy and a lot of computing power. The process works similarly to how it does for humans: someone who has mastered learning to ride a bicycle will find it easier to learn to ride a motorbike because many principles are similar.
In companies, knowledge transfer between models enables new tasks to be solved faster. For example, a model that understands language can help train another system for speech recognition in different dialects.
Knowledge transfer between models makes projects in the mentioned categories more efficient by making sensible use of existing knowledge, rather than starting from scratch.
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