Deep Learning

Using Classic CNN Methods to Build an Automatic Extraction Model for Road Elements in Guiyang

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Using Classic CNN Methods to Build an Automatic Extraction Model for Road Elements in Guiyang

Using classic CNN methods to build an automatic extraction model for road elements in Guiyang She Zuoming, Shen Yongzhi, Song Jianhong, Xiang Yuchen Guiyang Surveying and Mapping Institute, Guiyang, Guizhou 550000 Keywords: CNN, Deep Learning, Road Extraction, Remote Sensing Interpretation Citation format: She Zuoming, Shen Yongzhi, Song Jianhong, et al. Using classic CNN methods to … Read more

Deep Dive Into VGGNet: A Classic CNN Architecture

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Deep Dive Into VGGNet: A Classic CNN Architecture

In 2014, the Visual Geometry Group at the University of Oxford and Google DeepMind developed a new convolutional neural network called VGGNet. VGGNet is a deeper deep convolutional neural network than AlexNet, and this model achieved second place in the 2014 ILSVRC competition, with GoogLeNet taking first place (which we will introduce later). Paper: Very … Read more

Bold and Innovative Neural Network Structures in CNN

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Bold and Innovative Neural Network Structures in CNN

Click the above “AI Youdao” and select “Star” public account Heavyweight content delivered immediately Editor: Yi Zhen https://www.zhihu.com/question/337470480 This article is for academic sharing only. If there is an infringement, it will be deleted. Reports on machine learning algorithms and natural language processing What Bold and Innovative Neural Network Structures Exist in Convolutional Neural Networks? … Read more

Implementing VGGNet with PyTorch: A Practical Guide

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Implementing VGGNet with PyTorch: A Practical Guide

Hello everyone, I am Redstone! In the previous article: Implementing VGGNet (Theoretical Part) We detailed the network structure of VGGNet. Today, we will use PyTorch to reproduce the VGGNet network and apply the VGGNet model to solve a classic Kaggle image recognition competition problem. Let’s get started! 1. Dataset Preparation In the paper, the authors … Read more

Evolution of CNN Architecture: From AlexNet to ResNet

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Evolution of CNN Architecture: From AlexNet to ResNet

Evolution of CNN Architecture: From AlexNet to ResNet Hello everyone, I am Sister Liu. Today we will delve into the evolution of Convolutional Neural Networks (CNN), which is one of the most important technological developments in the field of computer vision. Background Knowledge Before the rise of deep learning, traditional image recognition methods relied on … Read more

The Best Performing CNN Architecture – DenseNet

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The Best Performing CNN Architecture - DenseNet

Densely Connected Convolutional Networks Comparison with ResNet Implementation code in various languages: Architecture diagram: If the image below is unclear, you can visit this link for the first Keras implementation mentioned above. Paper: This article is recommended by zdx3578. Let’s learn and discuss together: QQ group number 325921031; WeChat group, please leave a message in … Read more

R-CNN Series of Object Detection Networks

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R-CNN Series of Object Detection Networks

R-CNN series object detection networks are the first series of networks in the field of object detection using deep learning, serving as a typical Two-Stage object detection network. This series includes R-CNN, Fast R-CNN, and Faster R-CNN, and as their names suggest, each generation is faster than the previous one, primarily because the characteristic of … Read more

Implementing CNN From Scratch: Understanding the Mathematical Essence

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Implementing CNN From Scratch: Understanding the Mathematical Essence

Selected from arXiv Translated by Machine Heart Contributors: Huang Xiaotian, Lu Xue, Jiang Siyuan Recently, researchers from Nanyang Technological University published a paper describing the mathematical principles of convolutional networks. This paper explains the entire operation and propagation process of convolutional networks from a mathematical perspective. It is very helpful for understanding the mathematical essence … Read more

Understanding the Mathematical Principles Behind RNNs

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Understanding the Mathematical Principles Behind RNNs

0Introduction Nowadays, discussions about machine learning, deep learning, and artificial neural networks are becoming more and more prevalent. However, programmers often just want to use these magical frameworks without wanting to know how they actually work behind the scenes. But if we could grasp these underlying principles, wouldn’t it be better for us to use … Read more