Deformable Kernel
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Understanding Deformable Kernels
Deformable Kernels, or DKs, are a type of convolutional operator that allows for deformation modeling. They are…
Span-Based Dynamic Convolution
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Span-Based Dynamic Convolution is a cutting-edge technique used in the ConvBERT architecture to capture local dependencies between tokens. Unlike classic…
Mixed Depthwise Convolution
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Understanding MixConv: Mixing up Multiple Kernel Sizes
In the world of convolutional neural networks (CNNs), there is a type of…
ShapeConv
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Understanding ShapeConv: A Shape-aware Convolutional Layer for Depth Feature Processing in Indoor RGB-D Semantic Segmentation
ShapeConv is a type of…
PP-OCR
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Understanding PP-OCR: A Revolutionary OCR System
PP-OCR is an OCR system that comprises three main components, namely text detection, detected…
Switchable Atrous Convolution
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Overview of Switchable Atrous Convolution (SAC)
Switchable Atrous Convolution (SAC) is a technique used in computer vision to improve the…
Dimension-wise Convolution
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Dimension-wise Convolution, also known as DimConv, is a specialized type of convolution that encodes depth-wise, height-wise, and width-wise information independently.…
Dilated convolution with learnable spacings
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Overview of Dilated Convolution with Learnable Spacings (DCLS)
Dilated Convolution with Learnable Spacings, or DCLS, is a new technique that…
Attention-augmented Convolution
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Introduction to Attention-augmented Convolution
Attention-augmented Convolution is a type of convolutional neural network that utilizes a two-dimensional relative self-attention mechanism.…
CondConv
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What is CondConv and how does it work?
CondConv, short for Conditionally Parameterized Convolutions, is a type of convolutional neural…
Octave Convolution
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Octave Convolution (OctConv) is a method that reduces the memory and computation cost of storing and processing feature maps that…
Selective Kernel Convolution
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A Selective Kernel Convolution is a type of convolution that is used in deep learning to enable neurons to adjust…
Lightweight Convolution
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Explaining LightConv at an 8th Grade Level
LightConv is a way to analyze sequences of data, like music, speech, or…
Dynamic Convolution
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Dynamic convolution is a novel operator design that increases the representational power of lightweight CNNs, without increasing their computational cost…
Depthwise Dilated Separable Convolution
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A Depthwise Dilated Separable Convolution is a type of convolution used in deep learning that utilizes two different techniques to…
Groupwise Point Convolution
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A Groupwise Point Convolution is a special type of convolution that is used in image processing, computer vision, and deep…
Invertible 1x1 Convolution
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An invertible 1x1 convolution is a type of mathematical operation used in flow-based generative models. Its purpose is to reverse…
CoordConv
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CoordConv: An Extension to the Standard Convolutional Layer
CoordConv is a novel and simple extension to the standard convolutional layer…
Active Convolution
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Overview of Active Convolution
Active Convolution is a type of convolution that allows for a more flexible receptive field structure…
Submanifold Convolution
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Submanifold Convolution (SC) is a computer science technique used in tasks with sparse data, such as semantic segmentation of 3D…
Displaced Aggregation Units
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DAU-ConvNet is a new technology that is changing the way convolutional neural networks (ConvNets) work. The traditional method of using…
Spatially Separable Convolution
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Overview of Spatially Separable Convolution in Deep Learning
In the world of deep learning, convolution is one of the basic…
Masked Convolution
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Masked Convolution is a type of convolution that is used for image generation models. It is introduced with the PixelRNN…
(2+1)D Convolution
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When it comes to action recognition in convolutional neural networks, (2+1)D convolution is a popular and efficient technique…
Deformable Convolution
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Overview: Understanding Deformable Convolutions
Deformable convolutions are an innovative approach to the standard convolution process used in deep learning. This…
1-Dimensional Convolutional Neural Networks
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Introduction to 1D CNNs
1D CNNs are powerful machine learning models that are used to analyze signals and text. They…
Dilated Convolution
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Dilated Convolutions: An Introduction to a Powerful Deep Learning Technique
If you're familiar with convolution, you may have heard of…
Pointwise Convolution
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Pointwise Convolution is a method used in image processing that involves a small kernel, known as a 1x1 kernel which…
Depthwise Convolution
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Depthwise Convolution is a type of mathematical operation that is used in deep learning, a subfield of artificial intelligence that…
Depthwise Separable Convolution
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Convolution is one of the core building blocks of deep learning models. It involves applying a filter over an input…
Grouped Convolution
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What is Grouped Convolution?
A Grouped Convolution is a type of convolutional neural network (CNN) that uses multiple kernels per…
1x1 Convolution
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What is 1x1 Convolution?
If you’ve heard about computer vision, convolutional neural networks (CNNs), or deep learning, you may…
Convolution
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Understanding Convolution
Convolution is a type of matrix operation that is commonly used in image processing and computer vision. It…
3D Convolution
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What is 3D Convolution?
3D Convolution is a mathematical operation used in deep learning to analyze data with a third…