In the world of computer science and technology, MetaFormer is a buzzword that has been gaining popularity lately. So, what exactly is MetaFormer? It is a general architecture that is abstracted from Transformers by not specifying the token mixer.
What is Transformers?
If you are not familiar with Transformers, it is a neural network architecture that has been widely used in natural language processing (NLP) tasks, such as language translation, text generation, and sentiment analysis. One of
Overview of MLP-Mixer
The MLP-Mixer architecture, also known as Mixer, is an image architecture utilized for image classification tasks. What sets Mixer apart from other image architectures is that it doesn't rely on convolutions or self-attention to process images. Instead, Mixer uses multi-layer perceptrons (MLPs) that repeatedly apply across spatial locations or feature channels. This makes Mixer a unique and powerful image architecture.
How Mixer Works
At its core, Mixer takes a sequence
MobileNetV2: A Mobile-Optimized Convolutional Neural Network
A convolutional neural network (CNN) is a type of deep learning algorithm designed to recognize patterns in visual data. CNNs have proven powerful in many computer vision tasks. However, their size and compute requirements make it challenging to use in mobile devices with limited resources. To address this issue, MobileNetV2 was developed - a CNN architecture aimed at mobile devices, which prioritizes efficiency without sacrificing ac
PoolFormer is a machine learning tool that is used to verify the effectiveness of MetaFormer compared to Attention-Based Neural Networks. It is a simple operator, but it plays a critical role in determining the performance of MetaFormer.
What is Pooling?
Pooling is a technique that is commonly used in neural networks. The purpose of pooling is to reduce the dimensionality of the input, without losing important features of the data. Pooling is typically applied after a convolutional layer, but
ProxylessNet-CPU is a newly developed image model that utilizes cutting-edge technology to deliver optimized performance for CPU devices. The model was created using the ProxylessNAS neural architecture search algorithm, which enables it to perform exceptionally well on CPU devices. The basic building block of ProxylessNet-CPU is the inverted residual block, also known as MBConvs, which was first introduced in MobileNetV2. In this article, we will delve deeper into what ProxylessNet-CPU is, how
Overview of ProxylessNet-GPU
ProxylessNet-GPU is a type of convolutional neural network architecture that is designed to work well on GPU devices. This network was created using a technique called neural architecture search, which automatically discovers the best architecture for the network based on the given constraints and objectives. In this case, the ProxylessNAS algorithm was used to discover the best architecture for a neural network that can be optimized for GPU devices.
How Proxyless
ProxylessNet-Mobile is a type of convolutional neural architecture that has been specifically designed for use on mobile devices. This architecture was developed using the ProxylessNAS (neural architecture search) algorithm, which helps to optimize the architecture for mobile devices. The basic building block of this architecture is the inverted residual blocks, also known as MBConvs, which have been taken from MobileNetV2. The efficient design of this architecture makes it an ideal solution for
The Pyramid Vision Transformer v2 (PVTv2) is an advanced technology used in detection and segmentation tasks. This state-of-the-art system improves on its predecessor, PVTv1, through better design features, including overlapping patch embedding, convolutional feed-forward networks, and linear complexity attention layers that are orthogonal to the PVTv1 framework.
What is a Vision Transformer?
A Vision Transformer is an artificial intelligence technology that uses transformers, which are a typ
What is Res2Net?
Res2Net is a type of image model that uses a variation on bottleneck residual blocks to represent features at multiple scales. It employs a novel building block for Convolutional Neural Networks (CNNs) that creates hierarchical residual-like connections within a single residual block. This enhances multi-scale feature representation at a granular level and increases the receptive field range for each network layer.
How does Res2Net Work?
Res2Net uses a new hierarchical build
Overview of Residual Multi-Layer Perceptrons (ResMLP)
Residual Multi-Layer Perceptrons, or ResMLP for short, is a type of architecture used for image classification. ResMLP is built entirely on multi-layer perceptrons, which are algorithms used in machine learning to create artificial neural networks that learn from data input. The ResMLP architecture is a simple residual network that alternates a linear layer and a feed-forward network in which channels interact independently per patch.
The R
Understanding ResNeSt
ResNeSt is a variant of ResNet, which is a deep artificial neural network used for image recognition tasks. It stands for Residual Neural Network and has been used in various applications, including speech recognition, natural language processing, and computer vision. ResNet learns to identify images by stacking residual blocks, which allows for more accurate and efficient image recognition. The ResNeSt model differs from ResNet in that it stacks split-attention blocks ins
**** Self-Attention Network or SANet is a type of neural network that uses self-attention modules to identify features in images for image recognition. Image recognition is a critical part of computer vision, and SANet is one of the advanced techniques used to achieve this. **
The Basics of Self-Attention Networks (SANet)
** Self-Attention Networks are a type of neural network that compute attention weights for all positions in the input sequence, which in the case of image recognition, is th
The Swin Transformer: A Breakthrough in Image Processing
In recent years, computer vision tasks such as image classification and object detection have seen tremendous improvements. One of the key factors that has driven these improvements is the development of transformer models, a type of deep learning architecture that has been successful in natural language processing tasks such as language translation.
The Swin Transformer is a recent addition to this family of models, and it represents a
T2T-ViT, also known as Tokens-To-Token Vision Transformer, is an innovative technology that is designed to enhance image recognition processes. This technology incorporates two main elements: a specialized layerwise Tokens-to-Token Transformation technique and an efficient backbone structure for vision transformation.
What is T2T-ViT?
T2T-ViT is a variant of the widely used Vision Transformer (ViT) technology. ViT is a type of deep neural network system that has been developed specifically fo
The topic of TNT is an innovative approach to computer vision technology that utilizes a self-attention-based neural network called Transformer to process both patch-level and pixel-level representations of images. This novel Transformer-iN-Transformer (TNT) model uses an outer transformer block to process patch embeddings and an inner transformer block to extract local features from pixel embeddings, thereby allowing for a more comprehensive view of the image features. Ultimately, the TNT model
Introduction to Vision Transformer
The Vision Transformer, also known as ViT, is a model used for image classification that utilizes a Transformer-like architecture over patches of an image. This approach splits the image into fixed-size patches, and each patch is linearly embedded, added with position embeddings, and then fed into a standard Transformer encoder. To perform classification, an extra learnable "classification token" is added to the sequence.
What is a Transformer?
A Transforme
WideResNet: A High-Performing Variant on Residual Networks
In recent years, the field of deep learning has seen tremendous progress with the development of convolutional neural networks (CNNs). They have been used in various applications such as image recognition, natural language processing, and speech recognition, to name a few.
One of the most successful deep architectures, ResNets, was introduced in 2015. Since its inception, ResNets have consistently outperformed the previous state-of-the