ResNet: Enabling Deep Convolutional Neural Networks through Residual Learning

Xingyu Liu, Kun Ming Goh
10/28/2025
cs.CVcs.AI

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

Convolutional Neural Networks (CNNs) has revolutionized computer vision, but training very deep networks has been challenging due to the vanishing gradient problem. This paper explores Residual Networks (ResNet), introduced by He et al. (2015), which overcomes this limitation by using skip connections. ResNet enables the training of networks with hundreds of layers by allowing gradients to flow directly through shortcut connections that bypass intermediate layers. In our implementation on the CIFAR-10 dataset, ResNet-18 achieves 89.9% accuracy compared to 84.1% for a traditional deep CNN of similar depth, while also converging faster and training more stably.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Algebraic Representations for Faster Predictions in Convolutional Neural Networks

90% match1 repo2024

Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey

85% match1 repo82024

Make Deep Networks Shallow Again

84% match2023

Densely Connected Convolutional Networks

83% match1 repo4.9k2016

Scaling Supervised Local Learning with Augmented Auxiliary Networks

82% match1 repo62026

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

82% match2 repos22.1k2019

Cite this paper

@article{liu2025resnet,
  title  = {ResNet: Enabling Deep Convolutional Neural Networks through Residual Learning},
  author = {Xingyu Liu and Kun Ming Goh},
  year   = {2025},
  eprint = {2510.24036},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.24036v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2510.24036.svg)](https://codeofpaper.com/paper/2510.24036)

Discussion