# Efficient and Flexible Method for Reducing Moderate-Size Deep Neural Networks with Condensation

## TL;DR

This paper introduces a method to shrink moderate-sized neural networks while keeping their performance, using a phenomenon called condensation.

## Contribution

A novel condensation-based reduction method is proposed for both fully connected and convolutional networks.

## Key findings

- The method reduced network size to 41.7% in combustion tasks without losing accuracy.
- In image classification, network size was reduced to 11.5% with satisfactory accuracy.
- The approach is broadly applicable to most trained neural networks.

## Abstract

Neural networks have been extensively applied to a variety of tasks, achieving astounding results. Applying neural networks in the scientific field is an important research direction that is gaining increasing attention. In scientific applications, the scale of neural networks is generally moderate size, mainly to ensure the speed of inference during application. Additionally, comparing neural networks to traditional algorithms in scientific applications is inevitable. These applications often require rapid computations, making the reduction in neural network sizes increasingly important. Existing work has found that the powerful capabilities of neural networks are primarily due to their nonlinearity. Theoretical work has discovered that under strong nonlinearity, neurons in the same layer tend to behave similarly, a phenomenon known as condensation. Condensation offers an opportunity to reduce the scale of neural networks to a smaller subnetwork with a similar performance. In this article, we propose a condensation reduction method to verify the feasibility of this idea in practical problems, thereby validating existing theories. Our reduction method can currently be applied to both fully connected networks and convolutional networks, achieving positive results. In complex combustion acceleration tasks, we reduced the size of the neural network to 41.7% of its original scale while maintaining prediction accuracy. In the CIFAR10 image classification task, we reduced the network size to 11.5% of the original scale, still maintaining a satisfactory validation accuracy. Our method can be applied to most trained neural networks, reducing computational pressure and improving inference speed.