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Training a smaller "student" model to imitate a larger "teacher", transferring much of its capability into a cheaper, faster model.
Hinton et al. (2015) showed that a compact model can be trained on the soft probability outputs of a larger model or ensemble, learning from the teacher's full distribution rather than just hard labels. The student captures much of the teacher's behaviour at a fraction of the size and cost.
Distillation is a standard way to ship small, fast versions of large models for latency- or cost-sensitive deployment.
Primary, peer-reviewed and archival sources for this definition.
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