Dynamic Model Pruning with Feedback

Understand

Deep neural networks often have millions of parameters.

• This can hinder their deployment to low-end devices, not only due to high memory requirements but also because of increased latency at inference.
• We propose a novel model compression method that generates a sparse trained model without additional overhead: by allowing (i) dynamic allocation of the sparsity pattern and (ii) incorporating feedback signal to reactivate prematurely pruned weights we obtain a performant sparse model in one single training pass (retraining is not needed, but can further improve the performance).
• We evaluate our method on CIFAR-10 and ImageNet, and show that the obtained sparse models can reach the state-of-the-art performance of dense models.