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

Abstract: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 snapshot models. Building on the contrastive decomposition of the CAP framework (Xu et al., 2022), we provide a rigorous matched-budget characterization of this approach across multiple pruning criteria. Evaluated across 90 settings, BaCP improves accuracy substantially in extreme sparsity regimes where standard pruning fails, and is close to baseline where representations remain intact.

Machine Learning (cs.LG)

arXiv:2610.02524 [cs.LG]

(or arXiv:2610.02524v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.02524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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