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A machine learning engineer is training a small 'student' model to mimic a large 'teacher' model. The training process aims to minimize the Kullback-Leibler (KL) divergence between the teacher's output probability distribution (P_teacher) and the student's (P_student), formulated as: Loss = KL(P_teacher || P_student). Based on the properties of this specific formulation, what is the primary effect of minimizing this loss on the student model's behavior?
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A machine learning engineer is training a small 'student' model to mimic a large 'teacher' model. The training process aims to minimize the Kullback-Leibler (KL) divergence between the teacher's output probability distribution (P_teacher) and the student's (P_student), formulated as:
Loss = KL(P_teacher || P_student). Based on the properties of this specific formulation, what is the primary effect of minimizing this loss on the student model's behavior?Interpreting KL Divergence Loss in Knowledge Distillation
Evaluating Student Model Performance in Knowledge Distillation