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

arXiv:2610.02524 · cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.LG

Submitted: 2026-10-01

Updated: 2026-10-01

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Pruning — 1× in this paper · explained in 4 episodes

Sources

• Studying the impact of magnitude pruning on contrastive learning methods
• The State of Sparsity in Deep Neural Networks
• What Do Compressed Deep Neural Networks Forget?
• Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning
• Pruning via Iterative Ranking of Sensitivity Statistics
• To prune, or not to prune: exploring the efficacy of pruning for model compression

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks