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There is an increasing interest on accelerating neural networks for real-timeapplications. We study the student-teacher strategy, in which a small and faststudent network is trained with the auxiliary information learned from a largeand accurate teacher network. We propose to use conditional adversarialnetworks to learn the loss function to transfer knowledge from teacher tostudent. The proposed method is particularly effective for relatively smallstudent networks. Moreover, experimental results show the effect of networksize when the modern networks are used as student. We empirically study thetrade-off between inference time and classification accuracy, and providesuggestions on choosing a proper student network.

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks