To measure how much the ensemble’s knowledge actually transfers, we run three models against the same held-out test set. The ensemble — all 12 teachers voting together via averaged logits — sets the accuracy ceiling at 97.80%. This is the number we are trying to approximate, not beat. The baseline student is an identical single-model architecture trained the conventional way, on hard labels only: it sees each sample as a binary 0 or 1, nothing more. It lands at 96.50%. The distilled student is the same architecture again, but trained on the ensemble’s soft probability outputs at temperature T=3, with a combined loss weighted 70% toward matching the teacher’s distribution and 30% toward ground truth labels. It reaches 97.20%.
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