# Retrain or not retrain? -- efficient pruning methods of deep CNN networks

Source: [Retrain or not retrain? -- efficient pruning methods of deep CNN networks](<https://www.sota2.com/research/paper/68fa67051e209131cea13d8e>)

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

Data updated: 2025-10-25T22:04:19.938Z

Authors: Marcin Pietron, Maciej Wielgosz

Published: 2020-02-12T23:24:28.000Z

## Abstract

Convolutional neural networks (CNN) play a major role in image processing tasks like image classification, object detection, semantic segmentation. Very often CNN networks have from several to hundred stacked layers with several megabytes of weights. One of the possible methods to reduce complexity and memory footprint is pruning. Pruning is a process of removing weights which connect neurons from two adjacent layers in the network. The process of finding near optimal solution with specified drop in accuracy can be more sophisticated when DL model has higher number of convolutional layers. In the paper few approaches based on retraining and no retraining are described and compared together.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2002.07051>)

[PDF](<https://arxiv.org/pdf/2002.07051.pdf>)