# SCOP: Scientific Control for Reliable Neural Network Pruning

arXiv: https://arxiv.org/abs/2010.10732 · HTML: https://mlsanity.com/paper/2010.10732/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 30 of 93)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Patch Slimming for Efficient Vision Transformers](https://mlsanity.com/paper/2106.02852/) (2021)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [Distilling Object Detectors via Decoupled Features](https://mlsanity.com/paper/2103.14475/) (2021)
- [Vision Transformer Pruning](https://mlsanity.com/paper/2104.08500/) (2021)
- [Unified Visual Transformer Compression](https://mlsanity.com/paper/2203.08243/) (2022)
- [CHIP: CHannel Independence-based Pruning for Compact Neural Networks](https://mlsanity.com/paper/2110.13981/) (2021)
- [Only Train Once: A One-Shot Neural Network Training And Pruning Framework](https://mlsanity.com/paper/2107.07467/) (2021)
- [X-Pruner: eXplainable Pruning for Vision Transformers](https://mlsanity.com/paper/2303.04935/) (2023)
- [A Survey on Transformer Compression](https://mlsanity.com/paper/2402.05964/) (2024)
- [Efficient Lottery Ticket Finding: Less Data is More](https://mlsanity.com/paper/2106.03225/) (2021)
- [GhostNetV2: Enhance Cheap Operation with Long-Range Attention](https://mlsanity.com/paper/2211.12905/) (2022)
- [Manifold Regularized Dynamic Network Pruning](https://mlsanity.com/paper/2103.05861/) (2021)
- [Learning Frequency Domain Approximation for Binary Neural Networks](https://mlsanity.com/paper/2103.00841/) (2021)
- [Dynamic Sparse Training with Structured Sparsity](https://mlsanity.com/paper/2305.02299/) (2023)
- [CHEX: CHannel EXploration for CNN Model Compression](https://mlsanity.com/paper/2203.15794/) (2022)
- [Dynamic Resolution Network](https://mlsanity.com/paper/2106.02898/) (2021)
- [Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning](https://mlsanity.com/paper/2102.03214/) (2021)
- [SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning](https://mlsanity.com/paper/2110.11395/) (2021)
- [GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization](https://mlsanity.com/paper/2109.02220/) (2021)
- [Coarsening the Granularity: Towards Structurally Sparse Lottery Tickets](https://mlsanity.com/paper/2202.04736/) (2022)
- [Effective Model Sparsification by Scheduled Grow-and-Prune Methods](https://mlsanity.com/paper/2106.09857/) (2021)
- [Fire Together Wire Together: A Dynamic Pruning Approach with Self-Supervised Mask Prediction](https://mlsanity.com/paper/2110.08232/) (2021)
- [BCNet: Searching for Network Width with Bilaterally Coupled Network](https://mlsanity.com/paper/2105.10533/) (2021)
- [HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks](https://mlsanity.com/paper/2301.09422/) (2023)
- [Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch](https://mlsanity.com/paper/2403.14729/) (2024)
- [RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks](https://mlsanity.com/paper/2105.14797/) (2021)
- [RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging](https://mlsanity.com/paper/2110.01397/) (2021)
- [Dynamic Sparsity Is Channel-Level Sparsity Learner](https://mlsanity.com/paper/2305.19454/) (2023)
- [OSSCAR: One-Shot Structured Pruning in Vision and Language Models with Combinatorial Optimization](https://mlsanity.com/paper/2403.12983/) (2024)

## Abstract (arXiv)

This paper proposes a reliable neural network pruning algorithm by setting up a scientific control. Existing pruning methods have developed various hypotheses to approximate the importance of filters to the network and then execute filter pruning accordingly. To increase the reliability of the results, we prefer to have a more rigorous research design by including a scientific control group as an essential part to minimize the effect of all factors except the association between the filter and expected network output. Acting as a control group, knockoff feature is generated to mimic the feature map produced by the network filter, but they are conditionally independent of the example label given the real feature map. We theoretically suggest that the knockoff condition can be approximately preserved given the information propagation of network layers. Besides the real feature map on an intermediate layer, the corresponding knockoff feature is brought in as another auxiliary input signal for the subsequent layers. Redundant filters can be discovered in the adversarial process of different features. Through experiments, we demonstrate the superiority of the proposed algorithm over state-of-the-art methods. For example, our method can reduce 57.8% parameters and 60.2% FLOPs of ResNet-101 with only 0.01% top-1 accuracy loss on ImageNet. The code is available at https://github.com/huawei-noah/Pruning/tree/master/SCOP_NeurIPS2020.