Knowledge Distillation for Model Compression
Knowledge distillation is a technique where a smaller, 'student' model learns to mimic the behavior of a larger, more complex 'teacher' model. Instead of just learning from labeled examples, the student also learns from the teacher's 'soft targets' (probability distributions over classes), capturing the teacher's nuanced understanding. This allows for deploying highly accurate, but more efficient, models.
It's like a seasoned mentor (teacher) not just giving answers, but also sharing their thought process with a junior colleague (student) to accelerate their learning.
It enables the deployment of powerful AI models on resource-constrained devices, broadening AI accessibility.
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