# Channel Pruning via Automatic Structure Search

arXiv: https://arxiv.org/abs/2001.08565 · HTML: https://mlsanity.com/paper/2001.08565/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2020.

## Built on by (most cited first, 30 of 85)

- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [Federated Unlearning via Class-Discriminative Pruning](https://mlsanity.com/paper/2110.11794/) (2021)
- [Convolutional Neural Network Pruning with Structural Redundancy Reduction](https://mlsanity.com/paper/2104.03438/) (2021)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models](https://mlsanity.com/paper/2410.10912/) (2024)
- [Learning Best Combination for Efficient N:M Sparsity](https://mlsanity.com/paper/2206.06662/) (2022)
- [SepPrune: Structured Pruning for Efficient Deep Speech Separation](https://mlsanity.com/paper/2505.12079/) (2025)
- [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)
- [Carrying out CNN Channel Pruning in a White Box](https://mlsanity.com/paper/2104.11883/) (2021)
- [Isomorphic Pruning for Vision Models](https://mlsanity.com/paper/2407.04616/) (2024)
- [Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch](https://mlsanity.com/paper/2403.14729/) (2024)
- [SiMaN: Sign-to-Magnitude Network Binarization](https://mlsanity.com/paper/2102.07981/) (2021)
- [An Information Theory-inspired Strategy for Automatic Network Pruning](https://mlsanity.com/paper/2108.08532/) (2021)
- [Adaptive Sharpness-Aware Pruning for Robust Sparse Networks](https://mlsanity.com/paper/2306.14306/) (2023)
- [Network Pruning using Adaptive Exemplar Filters](https://mlsanity.com/paper/2101.07985/) (2021)
- [1xN Pattern for Pruning Convolutional Neural Networks](https://mlsanity.com/paper/2105.14713/) (2021)
- [Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters](https://mlsanity.com/paper/2202.07190/) (2022)
- [GhostNets on Heterogeneous Devices via Cheap Operations](https://mlsanity.com/paper/2201.03297/) (2022)
- [Towards Lightweight Super-Resolution with Dual Regression Learning](https://mlsanity.com/paper/2207.07929/) (2022)
- [SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks](https://mlsanity.com/paper/2102.03773/) (2021)
- [Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps](https://mlsanity.com/paper/2209.02869/) (2022)
- [Performance-aware Approximation of Global Channel Pruning for Multitask CNNs](https://mlsanity.com/paper/2303.11923/) (2023)
- [RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration](https://mlsanity.com/paper/2411.06463/) (2024)
- [HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels](https://mlsanity.com/paper/2202.10716/) (2022)
- [EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models](https://mlsanity.com/paper/2503.15369/) (2025)
- [EZCrop: Energy-Zoned Channels for Robust Output Pruning](https://mlsanity.com/paper/2105.03679/) (2021)
- [Model Pruning Based on Quantified Similarity of Feature Maps](https://mlsanity.com/paper/2105.06052/) (2021)
- [CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization](https://mlsanity.com/paper/2110.10921/) (2021)
- [ACORT: A Compact Object Relation Transformer for Parameter Efficient Image Captioning](https://mlsanity.com/paper/2202.05451/) (2022)

## Abstract (arXiv)

Channel pruning is among the predominant approaches to compress deep neural networks. To this end, most existing pruning methods focus on selecting channels (filters) by importance/optimization or regularization based on rule-of-thumb designs, which defects in sub-optimal pruning. In this paper, we propose a new channel pruning method based on artificial bee colony algorithm (ABC), dubbed as ABCPruner, which aims to efficiently find optimal pruned structure, i.e., channel number in each layer, rather than selecting "important" channels as previous works did. To solve the intractably huge combinations of pruned structure for deep networks, we first propose to shrink the combinations where the preserved channels are limited to a specific space, thus the combinations of pruned structure can be significantly reduced. And then, we formulate the search of optimal pruned structure as an optimization problem and integrate the ABC algorithm to solve it in an automatic manner to lessen human interference. ABCPruner has been demonstrated to be more effective, which also enables the fine-tuning to be conducted efficiently in an end-to-end manner. The source codes can be available at https://github.com/lmbxmu/ABCPruner.