Convolution

Beyond ConvNeXt! Transformer-Style Convolutional Network Visual Baseline Model Conv2Former

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Beyond ConvNeXt! Transformer-Style Convolutional Network Visual Baseline Model Conv2Former

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP master’s and doctoral students, university teachers, and corporate researchers.The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning in China and abroad, especially for beginners. … Read more

Understanding CNN Convolution and Pooling Layers

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Understanding CNN Convolution and Pooling Layers

Click the above“Beginner Learning Vision“, choose to add “Starred” or “Pinned“ Important content delivered in real time Overview In deep learning, CNN networks are core components. For CNN networks, the calculations of convolutional layers and pooling layers are crucial. Different strides, padding methods, kernel sizes, and pooling strategies can significantly impact the final output model, … Read more

Involution: A Powerful New Operator for Neural Networks

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Involution: A Powerful New Operator for Neural Networks

Machine Heart Release Author: Li Duo This work was mainly completed by me and Hu Jie, the author of SENet. I would also like to thank my two mentors at HKUST, Chen Qifeng and Zhang Tong, for their discussions and suggestions. This article introduces our paper accepted at CVPR 2021, Involution: Inverting the Inherence of … Read more

Do CNNs Really Need Downsampling (Upsampling)?

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Do CNNs Really Need Downsampling (Upsampling)?

Click on the above“Learn Visuals” to selectStar or “Top” Heavyweight content, delivered first Background Introduction In common convolutional neural networks, sampling is almost everywhere, previously max pooling, now strided convolution. Taking the VGG network as an example, it uses quite a bit of max pooling. The input side is on the left (the bottom has … Read more

Overview of CNN Convolution Methods

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Overview of CNN Convolution Methods

Click the above “Beginner Learning Vision” to choose to add “Starred” or “Pinned“ Important content delivered at the first time The Essence of Convolution Conventional Convolution Single-channel Convolution Multi-channel Convolution 3D Convolution Transposed Convolution 1×1 Convolution Depthwise Separable Convolution Dilated Convolution The Essence of Convolution Before introducing various convolutions, it is necessary to revisit the … Read more

Overview of Various Convolution Operations in CNNs

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Overview of Various Convolution Operations in CNNs

Are you a new friend? Remember to click the blue text to follow me~ The official account has changed its push rules, remember to click “Looking” after reading~ Next time a new article will appear in your subscription list in a timely manner Author | Captain Note | The official account is responsible for pushing … Read more

Top 10 Amazing Operations in Convolutional Neural Networks

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Top 10 Amazing Operations in Convolutional Neural Networks

Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered first! Author: Justin Ho Editor: Xixiaoyao’s Cute Shop Source: https://zhuanlan.zhihu.com/p/28749411 Introduction The CNN has evolved from AlexNet in 2012 to various CNN models invented by scientists, each deeper, more accurate, and lighter than the last. Below, we will briefly review some revolutionary works in … Read more

Derivation of Backpropagation Algorithm in Convolutional Neural Networks (CNN)

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Derivation of Backpropagation Algorithm in Convolutional Neural Networks (CNN)

Author: Nan Ke Yi Meng Ning Chen Lun @ Zhihu (Authorized) Editor: Jishi Platform Source: https://zhuanlan.zhihu.com/p/61898234 The mathematical derivation of backpropagation in multilayer perceptrons is mainly represented using mathematical formulas. In fully connected neural networks, these are not complicated, and even purely mathematical formulas are relatively easy to understand.However, convolutional neural networks are relatively more … Read more

Understanding Convolutional Neural Networks (CNN)

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Understanding Convolutional Neural Networks (CNN)

Source: Original article by CSDN blogger “Fate_fjh”. The traditional CNN network can only provide the LABEL of an image, but in many cases, it is necessary to segment the identified objects to achieve end to end recognition. This is where FCN comes in, providing a very important solution for object segmentation, with the core being … Read more

Mathematical Principles of Convolutional Neural Networks (CNN)

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Mathematical Principles of Convolutional Neural Networks (CNN)

This article shares an analysis of the mathematical principles of CNNs, which will help you deepen your understanding of how neural networks work in CNNs. As a recommendation, this article will include quite complex mathematical equations; if you are not familiar with linear algebra and calculus, that’s okay. The goal is not to memorize these … Read more