Knowledge transfer in AI is the process by which knowledge from an already
trained AI model is leveraged for another task or transferred to another model, allowing the reuse of previous learning and reducing resources needed to
train from scratch.
This concept is fundamental for improving the efficiency of
AI model development. Just as when a person applies previous knowledge to learn something new more quickly, knowledge transfer allows models to leverage prior learning.
In one of its forms, transfer learning, a model
trained on a general task is adjusted for a specific task, such as moving from general image recognition to identifying specific species.
Another key technique is
model distillation, where a large model transfers its knowledge to a smaller one, maintaining performance with lower computational consumption.
Fine-tuning specializes a pre-
trained model for a specific task without
training it from scratch, reducing data and costs. Multi-task
training allows learning several tasks simultaneously, optimizing generalization capability.
Knowledge transfer facilitates the creation of more efficient, adaptable, and accessible models for various applications.