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Models & Research
Model distillation is a technique that simplifies complex AI models to make them faster and more efficient without losing performance.
Model distillation involves training a smaller, more efficient model-called a student-to mimic the behavior of a larger, more complex model known as the teacher. The goal is to transfer the knowledge from the teacher to the student so that the student can perform tasks with similar accuracy but at a lower computational cost.
In practical applications, model distillation reduces the need for powerful hardware and large datasets, making AI accessible to more users and devices. This technique is particularly important in edge computing and mobile applications where resources are limited.
The process starts by training a high-capacity teacher model on a large dataset. Once trained, this model generates predictions or 'soft labels' for the student model to learn from. The student model, which has fewer parameters, is then trained to match these soft labels alongside the original hard labels. This transfer of knowledge allows the student to capture the essential features learned by the teacher.
✗ Model distillation always results in a significant loss of accuracy.
With proper training and optimization, model distillation can maintain high accuracy levels while significantly reducing computational requirements.
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