# COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning

arXiv: https://arxiv.org/abs/1906.10337 · HTML: https://mlsanity.com/paper/1906.10337/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [CP3: Channel Pruning Plug-in for Point-based Networks](https://mlsanity.com/paper/2303.13097/) (2023)
- [HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels](https://mlsanity.com/paper/2202.10716/) (2022)
- [On the Opportunities of Green Computing: A Survey](https://mlsanity.com/paper/2311.00447/) (2023)
- [Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning](https://mlsanity.com/paper/2209.13590/) (2022)
- [Structured Directional Pruning via Perturbation Orthogonal Projection](https://mlsanity.com/paper/2107.05328/) (2021)
- [EcoSpa: Efficient Transformer Training with Coupled Sparsity](https://mlsanity.com/paper/2511.11641/) (2025)
- [Reconstructing Pruned Filters using Cheap Spatial Transformations](https://mlsanity.com/paper/2110.12844/) (2021)
- [On Causal Inference for Data-free Structured Pruning](https://mlsanity.com/paper/2112.10229/) (2021)
- [A Comprehensive Study of Structural Pruning for Vision Models](https://mlsanity.com/paper/2406.12315/) (2024)
- [IPPRO: Importance-based Pruning with PRojective Offset for Magnitude-indifferent Structural Pruning](https://mlsanity.com/paper/2507.14171/) (2025)
- [Spectral Complex Autoencoder Pruning: A Fidelity-Guided Criterion for Extreme Structured Channel Compression](https://mlsanity.com/paper/2601.09352/) (2026)
- [Hierarchical Spatial and Channel Aggregation for Cross-domain Few-shot Segmentation](https://mlsanity.com/paper/2606.24296/) (2026)

## Abstract (arXiv)

Neural network compression empowers the effective yet unwieldy deep convolutional neural networks (CNN) to be deployed in resource-constrained scenarios. Most state-of-the-art approaches prune the model in filter-level according to the "importance" of filters. Despite their success, we notice they suffer from at least two of the following problems: 1) The redundancy among filters is not considered because the importance is evaluated independently. 2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer. Consequently, we must manually specify layer-wise pruning ratios. 3) They are prone to generate sub-optimal solutions because they neglect the inequality between reducing parameters and reducing computational cost. Reducing the same number of parameters in different positions in the network may reduce different computational cost. To address the above problems, we develop a novel algorithm named as COP (correlation-based pruning), which can detect the redundant filters efficiently. We enable the cross-layer filter comparison through global normalization. We add parameter-quantity and computational-cost regularization terms to the importance, which enables the users to customize the compression according to their preference (smaller or faster). Extensive experiments have shown COP outperforms the others significantly. The code is released at https://github.com/ZJULearning/COP.