# Blending Pruning Criteria for Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2107.05033 · HTML: https://mlsanity.com/paper/2107.05033/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 11 Jul 2021.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://mlsanity.com/paper/1707.06342/) (2017)
- [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://mlsanity.com/paper/1811.00250/) (2018)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning](https://mlsanity.com/paper/1903.10258/) (2019)
- [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://mlsanity.com/paper/1607.03250/) (2016)
- [Data-Driven Sparse Structure Selection for Deep Neural Networks](https://mlsanity.com/paper/1707.01213/) (2017)
- [An Entropy-based Pruning Method for CNN Compression](https://mlsanity.com/paper/1706.05791/) (2017)
- [DIANet: Dense-and-Implicit Attention Network](https://mlsanity.com/paper/1905.10671/) (2019)
- [Instance Enhancement Batch Normalization: an Adaptive Regulator of Batch Noise](https://mlsanity.com/paper/1908.04008/) (2019)
- [Efficient Attention Network: Accelerate Attention by Searching Where to Plug](https://mlsanity.com/paper/2011.14058/) (2020)
- [Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy](https://mlsanity.com/paper/1912.11527/) (2019)

## Built on by (most cited first, 13 of 13)

- [On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver](https://mlsanity.com/paper/2208.03680/) (2022)
- [Understanding Self-attention Mechanism via Dynamical System Perspective](https://mlsanity.com/paper/2308.09939/) (2023)
- [MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models](https://mlsanity.com/paper/2505.19959/) (2025)
- [Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima](https://mlsanity.com/paper/2402.11262/) (2024)
- [Switchable Self-attention Module](https://mlsanity.com/paper/2209.05680/) (2022)
- [Mix-Pooling Strategy for Attention Mechanism](https://mlsanity.com/paper/2208.10322/) (2022)
- [ASR: Attention-alike Structural Re-parameterization](https://mlsanity.com/paper/2304.06345/) (2023)
- [Efficient Climate Simulation via Machine Learning Method](https://mlsanity.com/paper/2209.08151/) (2022)
- [Causal Inference for Chatting Handoff](https://mlsanity.com/paper/2210.02862/) (2022)
- [Layer Pruning with Consensus: A Triple-Win Solution](https://mlsanity.com/paper/2411.14345/) (2024)
- [PrunePEFT: Iterative Hybrid Pruning for Parameter-Efficient Fine-tuning of LLMs](https://mlsanity.com/paper/2506.07587/) (2025)
- [Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models](https://mlsanity.com/paper/2609.12475/) (2026)
- [FedASAP: Activation Statistics-driven Structured Adaptive Pruning for Efficient Personalized Federated Learning for Lesion Segmentation on brain MRI](https://mlsanity.com/paper/2609.19042/) (2026)

## Abstract (arXiv)

The advancement of convolutional neural networks (CNNs) on various vision applications has attracted lots of attention. Yet the majority of CNNs are unable to satisfy the strict requirement for real-world deployment. To overcome this, the recent popular network pruning is an effective method to reduce the redundancy of the models. However, the ranking of filters according to their "importance" on different pruning criteria may be inconsistent. One filter could be important according to a certain criterion, while it is unnecessary according to another one, which indicates that each criterion is only a partial view of the comprehensive "importance". From this motivation, we propose a novel framework to integrate the existing filter pruning criteria by exploring the criteria diversity. The proposed framework contains two stages: Criteria Clustering and Filters Importance Calibration. First, we condense the pruning criteria via layerwise clustering based on the rank of "importance" score. Second, within each cluster, we propose a calibration factor to adjust their significance for each selected blending candidates and search for the optimal blending criterion via Evolutionary Algorithm. Quantitative results on the CIFAR-100 and ImageNet benchmarks show that our framework outperforms the state-of-the-art baselines, regrading to the compact model performance after pruning.