Comparing Rewinding and Fine-tuning in Neural Network Pruning

This research compares fine-tuning with weight rewinding and learning rate rewinding in neural network pruning.

Both rewinding techniques outperform fine-tuning, offering a network-agnostic approach that matches state-of-the-art pruning methods in terms of accuracy and compression ratios.