# Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy

Source: [Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy](<https://www.sota2.com/research/paper/68fa68d91e209131cea1ea73>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:47:52.778Z

Authors: Francisco Erivaldo Fernandes Junior, Gary G. Yen

Published: 2019-12-24T20:48:00.000Z

## Abstract

Currently, Deep Convolutional Neural Networks (DCNNs) are used to solve all kinds of problems in the field of machine learning and artificial intelligence due to their learning and adaptation capabilities. However, most successful DCNN models have a high computational complexity making them difficult to deploy on mobile or embedded platforms. This problem has prompted many researchers to develop algorithms and approaches to help reduce the computational complexity of such models. One of them is called filter pruning, where convolution filters are eliminated to reduce the number of parameters and, consequently, the computational complexity of the given model. In the present work, we propose a novel algorithm to perform filter pruning by using Multi-Objective Evolution Strategy (ES) algorithm, called DeepPruningES. Our approach avoids the need for using any knowledge during the pruning procedure and helps decision-makers by returning three pruned CNN models with different trade-offs between performance and computational complexity. We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DCNN architectures: Convolutional Neural Networks (CNNs), Residual Neural Networks (ResNets), and Densely Connected Neural Networks (DenseNets).

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-100 (Pruning Error Analysis)](<https://www.sota2.com/research/sota/image-classification-on-cifar-100-pruning-error-analysis.md>): Image Classification; CIFAR-100; Baseline Error: 41.94.

- [Image Classification on CIFAR-10 (test) (Pruning Error Analysis)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-pruning-error-analysis.md>): Image Classification; CIFAR-10 (test); Error after Pruning: 7.42.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.11527>)

[PDF](<https://arxiv.org/pdf/1912.11527.pdf>)