# Study of Residual Networks for Image Recognition

arXiv: https://arxiv.org/abs/1805.00325 · HTML: https://mlsanity.com/paper/1805.00325/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation](https://mlsanity.com/paper/2312.04713/) (2023)
- [MoRE: Multi-Modal Contrastive Pre-training with Transformers on X-Rays, ECGs, and Diagnostic Report](https://mlsanity.com/paper/2410.16239/) (2024)
- [GloNets: Globally Connected Neural Networks](https://mlsanity.com/paper/2311.15947/) (2023)
- [Biologically inspired deep residual networks for computer vision applications](https://mlsanity.com/paper/2205.02551/) (2022)

## Abstract (arXiv)

Deep neural networks demonstrate to have a high performance on image classification tasks while being more difficult to train. Due to the complexity and vanishing gradient problem, it normally takes a lot of time and more computational power to train deeper neural networks. Deep residual networks (ResNets) can make the training process faster and attain more accuracy compared to their equivalent neural networks. ResNets achieve this improvement by adding a simple skip connection parallel to the layers of convolutional neural networks. In this project we first design a ResNet model that can perform the image classification task on the Tiny ImageNet dataset with a high accuracy, then we compare the performance of this ResNet model with its equivalent Convolutional Network (ConvNet). Our findings illustrate that ResNets are more prone to overfitting despite their higher accuracy. Several methods to prevent overfitting such as adding dropout layers and stochastic augmentation of the training dataset has been studied in this work.