# Block Pruning for Enhanced Efficiency in Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2312.16904 · HTML: https://mlsanity.com/paper/2312.16904/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 11 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Data-Driven Sparse Structure Selection for Deep Neural Networks](https://mlsanity.com/paper/1707.01213/) (2017)
- [DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration](https://mlsanity.com/paper/1912.10178/) (2019)
- [To Filter Prune, or to Layer Prune, That Is The Question](https://mlsanity.com/paper/2007.05667/) (2020)
- [SR-init: An interpretable layer pruning method](https://mlsanity.com/paper/2303.07677/) (2023)

## Built on by (most cited first, 3 of 3)

- [MDP: Multidimensional Vision Model Pruning with Latency Constraint](https://mlsanity.com/paper/2504.02168/) (2025)
- [Multi-Dimensional Pruning: Joint Channel, Layer and Block Pruning with Latency Constraint](https://mlsanity.com/paper/2406.12079/) (2024)
- [Rethinking Theoretical Illumination for Efficient Low-Light Image Enhancement](https://mlsanity.com/paper/2409.05274/) (2024)

## Abstract (arXiv)

This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments. Our method diverges from traditional techniques that utilize proxy metrics, instead employing a direct block removal strategy to assess the impact on classification accuracy. This hands-on approach allows for an accurate evaluation of each block's importance. We conducted extensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using ResNet architectures. Our results demonstrate the efficacy of our method, particularly on large-scale datasets like ImageNet with ResNet50, where it excelled in reducing model size while retaining high accuracy, even when pruning a significant portion of the network. The findings underscore our method's capability in maintaining an optimal balance between model size and performance, especially in resource-constrained edge computing scenarios.