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Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch
Two fundamental observations are: (1) a larger teacher tends to produce probability vectors that are less distinct between non-ground-truth classes; (2) teachers with different capacities are basically consistent in their cognition of relative class affinity.
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Complex teachers tend to be over-confident and traditional temperature scaling limits the efficacy of {\it class discriminability}, resulting in less discriminative wrong class probabilities.
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