Weight Distillation: Transferring the Knowledge in Neural Network Parameters
This addresses the problem of efficient deployment of neural networks for practitioners, but it is incremental as it builds on existing knowledge distillation and parameter transfer methods.
The paper tackles model acceleration and compression by proposing Weight Distillation, which transfers knowledge from large to small networks via a parameter generator, resulting in a small network that is 1.88~2.94x faster with competitive performance and outperforms knowledge distillation by 0.51~1.82 BLEU points on machine translation tasks.
Knowledge distillation has been proven to be effective in model acceleration and compression. It allows a small network to learn to generalize in the same way as a large network. Recent successes in pre-training suggest the effectiveness of transferring model parameters. Inspired by this, we investigate methods of model acceleration and compression in another line of research. We propose Weight Distillation to transfer the knowledge in the large network parameters through a parameter generator. Our experiments on WMT16 En-Ro, NIST12 Zh-En, and WMT14 En-De machine translation tasks show that weight distillation can train a small network that is 1.88~2.94x faster than the large network but with competitive performance. With the same sized small network, weight distillation can outperform knowledge distillation by 0.51~1.82 BLEU points.