ConViT
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ConViT: A Game-changing Approach to Vision Transformers
ConViT is an innovation in the field of computer vision that has revolutionized…
PoolFormer
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PoolFormer is a machine learning tool that is used to verify the effectiveness of MetaFormer compared to Attention-Based Neural Networks.…
Dense Prediction Transformer
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Overview of Dense Prediction Transformers (DPT)
When it comes to analyzing images, one of the biggest challenges for computer programs…
Data-efficient Image Transformer
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What is DeiT?
DeiT stands for Data-Efficient Image Transformer. It is a type of Vision Transformer, which is a machine…
Bottleneck Transformer
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Understanding the Bottleneck Transformer
Recent advances in deep learning have led to significant impacts in the field of computer vision.…
MLP-Mixer
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Overview of MLP-Mixer
The MLP-Mixer architecture, also known as Mixer, is an image architecture utilized for image classification tasks. What…
LV-ViT
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Are you familiar with LV-ViT? It's a type of vision transformer that has been gaining attention in the field of…
LR-Net
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Introduction to LR-Net
LR-Net is a kind of neural network that is used for image feature extraction, which means it…
Residual Multi-Layer Perceptrons
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Overview of Residual Multi-Layer Perceptrons (ResMLP)
Residual Multi-Layer Perceptrons, or ResMLP for short, is a type of architecture used for…
gMLP
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gMLP is a new model that has been developed as an alternative to Transformers in the field of Natural Language…
ResNeSt
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Understanding ResNeSt
ResNeSt is a variant of ResNet, which is a deep artificial neural network used for image recognition tasks.…
Convolutional Vision Transformer
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Introduction to the Convolutional Vision Transformer (CvT)
The Convolutional Vision Transformer, or CvT for short, is a new type of…
Self-Attention Network
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**** Self-Attention Network or SANet is a type of neural network that uses self-attention modules to identify features in images for…
HaloNet
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What is HaloNet?
HaloNet is an advanced image classification model that uses a self-attention-based approach. It's designed to improve efficiency,…
Tokens-To-Token Vision Transformer
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T2T-ViT, also known as Tokens-To-Token Vision Transformer, is an innovative technology that is designed to enhance image recognition processes. This…
CrossViT
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CrossViT is a cutting-edge technology that makes use of vision transformers to extract multi-scale feature representations of images for classification…
MetaFormer
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In the world of computer science and technology, MetaFormer is a buzzword that has been gaining popularity lately. So, what…
DeepSIM
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Understanding DeepSIM: A Tool for Conditional Image Manipulation
If you've ever wanted to manipulate an image but found it difficult…
DeepViT
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DeepViT is an innovative way of enhancing the ViT (Vision Transformer) model. It replaces the self-attention layer with a re-attention…
IICNet
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An Overview of IICNet – An Invertible Image Conversion Net
Introduction:
With the growth of image-based tasks in the digital world,…
Swin Transformer
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The Swin Transformer: A Breakthrough in Image Processing
In recent years, computer vision tasks such as image classification and object…
Transformer in Transformer
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The topic of TNT is an innovative approach to computer vision technology that utilizes a self-attention-based neural network called Transformer…
Invertible Rescaling Network
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What is IRN?
Invertible Rescaling Network (IRN) is a type of network used for image rescaling. Image rescaling refers to…
ConvMLP
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ConvMLP is an advanced and sophisticated algorithm used for visual recognition. It is a combination of convolution layers and MLPs,…
Pyramid Vision Transformer v2
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The Pyramid Vision Transformer v2 (PVTv2) is an advanced technology used in detection and segmentation tasks. This state-of-the-art system improves…
Vision Transformer
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Introduction to Vision Transformer
The Vision Transformer, also known as ViT, is a model used for image classification that utilizes…
EfficientNet
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EfficientNet is a powerful convolutional neural network architecture and scaling method that is designed to uniformly scale all dimensions of…
Res2Net
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What is Res2Net?
Res2Net is a type of image model that uses a variation on bottleneck residual blocks to represent…
ProxylessNet-GPU
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Overview of ProxylessNet-GPU
ProxylessNet-GPU is a type of convolutional neural network architecture that is designed to work well on GPU…
ProxylessNet-CPU
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ProxylessNet-CPU is a newly developed image model that utilizes cutting-edge technology to deliver optimized performance for CPU devices. The model…
ProxylessNet-Mobile
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ProxylessNet-Mobile is a type of convolutional neural architecture that has been specifically designed for use on mobile devices. This architecture…
WideResNet
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WideResNet: A High-Performing Variant on Residual Networks
In recent years, the field of deep learning has seen tremendous progress with…
MobileNetV2
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MobileNetV2: A Mobile-Optimized Convolutional Neural Network
A convolutional neural network (CNN) is a type of deep learning algorithm designed to…
Interpretability
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Interpretability refers to the ability to understand and explain how a machine learning model works, including its decision-making process and…