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Knowledge distillation has been proven to be effective in model accelerationand compression. It allows a small network to learn to generalize in the sameway as a large network. Recent successes in pre-training suggest theeffectiveness of transferring model parameters. Inspired by this, weinvestigate methods of model acceleration and compression in another line ofresearch. We propose Weight Distillation to transfer the knowledge in the largenetwork parameters through a parameter generator. Our experiments on WMT16En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks show that weightdistillation can train a small network that is 1.88~2.94x faster than the largenetwork but with competitive performance. With the same sized small network,weight distillation can outperform knowledge distillation by 0.51~1.82 BLEUpoints.

Weight Distillation: Transferring the Knowledge in Neural Network Parameters