# A Novel Weight-Shared Multi-Stage CNN for Scale Robustness

## TL;DR

This paper introduces WSMS-Net, a multi-stage CNN architecture with shared weights, designed to improve scale robustness in image classification tasks, effectively enhancing existing CNNs like ResNet and DenseNet.

## Contribution

The paper proposes a novel weight-shared multi-stage CNN architecture that enhances scale robustness and can be integrated with existing deep CNNs.

## Key findings

- Improved accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets.
- Minor increase in parameters and computation time.
- Effective enhancement of scale robustness in CNNs.

## Abstract

Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness to the parallel shift of objects in images as well as their numerous parameters and the resulting high expression ability. However, CNNs have a limited robustness to other geometric transformations such as scaling and rotation. This limits the performance improvement of the deep CNNs, but there is no established solution. This study focuses on scale transformation and proposes a network architecture called the weight-shared multi-stage network (WSMS-Net), which consists of multiple stages of CNNs. The proposed WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet and enables them to acquire robustness to object scaling. Experimental results on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for image classification tasks with only a minor increase in the number of parameters and computation time.