BaCP: Backbone Contrastive Pruning for Preserving Representations in Extremely Sparse Neural Networks

Unstructured pruning at extreme sparsity often suffers from representational collapse, causing sharp drops in accuracy. To address this, we study Backbone Contrastive Pruning (BaCP), which regularizes the sparse network's embedding space by aligning it with pretrained, fine-tuned, and historical sna