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Bottleneck Transformer
Understanding the Bottleneck Transformer Recent advances in deep learning have led to significant impacts in the field of computer vision. One such development is the Bottleneck Transformer, commonly referred to as BoTNet. The BoTNet is an image classification model used for various computer vision tasks such as image classification, object detection, and instance segmentation. It is designed to improve the accuracy of these tasks while reducing the number of parameters and retaining low comput
ConViT
ConViT: A Game-changing Approach to Vision Transformers ConViT is an innovation in the field of computer vision that has revolutionized the use of vision transformers. A vision transformer is a type of machine learning model that uses attention mechanisms similar to those in natural language processing to analyze visual data. The idea behind ConViT is to use a gated positional self-attention module (GPSA) to enhance the performance of a vision transformer. The Basics of Vision Transformers I
ConvMLP
ConvMLP is an advanced and sophisticated algorithm used for visual recognition. It is a combination of convolution layers and MLPs, which makes it efficient in recognizing patterns, objects, and shapes in images. This algorithm is a hierarchical method that is designed by combining stages of convolution layers and MLPs to improve the accuracy and quality of visual recognition. What is ConvMLP? ConvMLP is a special type of neural network architecture used for image recognition. This algorithm
Convolutional Vision Transformer
Introduction to the Convolutional Vision Transformer (CvT) The Convolutional Vision Transformer, or CvT for short, is a new type of architecture that combines the best of both convolutional neural networks (CNNs) and Transformers. The CvT design introduces convolutions into two core sections of the ViT (Vision Transformer) architecture to achieve spatial downsampling and reduce semantic ambiguity in the attention mechanism. This allows the model to effectively capture local spatial contexts whi
CrossViT
CrossViT is a cutting-edge technology that makes use of vision transformers to extract multi-scale feature representations of images for classification purposes. Its dual-branch architecture combines image patches (or tokens) of various sizes to generate more robust visual features for image classification. Vision Transformer A vision transformer is a type of neural architecture that harnesses the power of self-attention in order to learn visual representations from unlabeled image data. The
Data-efficient Image Transformer
What is DeiT? DeiT stands for Data-Efficient Image Transformer. It is a type of Vision Transformer, which is a machine learning model used for image classification tasks. The DeiT model is designed specifically to train using a teacher-student strategy that relies on a distillation token. This token ensures that the student learns from the teacher through attention. How does DeiT Work? The DeiT model works by using a teacher-student strategy that relies on attention. The teacher is a larger,
DeepSIM
Understanding DeepSIM: A Tool for Conditional Image Manipulation If you've ever wanted to manipulate an image but found it difficult to do so using standard photo editing software, you might be interested in DeepSIM. DeepSIM is a generative model for conditional image manipulation based on a single image. The tool utilizes machine learning to map between a primitive representation of the image to the image itself so that users can make complex image changes easily by modifying the primitive inp
DeepViT
DeepViT is an innovative way of enhancing the ViT (Vision Transformer) model. It replaces the self-attention layer with a re-attention module to tackle the problem of attention collapse. In this way, it enables the user to train deeper ViTs. What is DeepViT? DeepViT is a modification of the ViT model. It is a vision transformer that uses re-attention modules instead of self-attention layers. The re-attention module has been developed to counteract the problem of attention collapse that can oc
Dense Prediction Transformer
Overview of Dense Prediction Transformers (DPT) When it comes to analyzing images, one of the biggest challenges for computer programs is being able to understand different parts of an image and make predictions about what they're seeing. Recently, a new type of technology has emerged with the potential to revolutionize how computers analyze and interpret image data: Dense Prediction Transformers (DPT). DPT is a type of vision transformer designed specifically for dense prediction tasks. These
EfficientNet
EfficientNet is a powerful convolutional neural network architecture and scaling method that is designed to uniformly scale all dimensions of depth, width, and resolution. The scaling is done using a compound coefficient, which differs from conventional methods that arbitrarily scale these factors. The scaling process involves increasing the network depth, width, and image size by fixed coefficients chosen through a small grid search on the original small model. EfficientNet uses a compound coef
gMLP
gMLP is a new model that has been developed as an alternative to Transformers in the field of Natural Language Processing (NLP). Instead of using self-attention processes, it consists of basic Multi-Layer Perceptron (MLP) layers with gating. The model is organized into a stack of blocks, each defined by a set of equations. The Structure of gMLP The gMLP model is composed of a stack of identical blocks, each of which has the following structure: 1. A linear projection to generate channel pro
HaloNet
What is HaloNet? HaloNet is an advanced image classification model that uses a self-attention-based approach. It's designed to improve efficiency, accuracy and speed when it comes to image classification. How Does HaloNet Work? At its core, HaloNet relies on a local self-attention architecture that can efficiently map to existing hardware with haloing. The formulation used in this model breaks translational equivariance, but the authors of the model say that it improves throughput and accura
IICNet
An Overview of IICNet – An Invertible Image Conversion Net Introduction: With the growth of image-based tasks in the digital world, it has become essential to have better image conversion techniques that can efficiently and accurately convert images into different forms. Invertible Image Conversion Net, or IICNet, is a unique framework developed to deal with reversible image conversion tasks. In this article, we will discuss the basics of IICNet, how it works, and some of its advantages. Wha
Interpretability
Interpretability refers to the ability to understand and explain how a machine learning model works, including its decision-making process and predictions. This is vital because it ensures that the model is making accurate and fair decisions, and allows humans to intervene and make necessary changes. Why is Interpretability important? Interpretability enables us to understand the reasoning behind the models and their predictions, especially if the models are used for critical decision making
Invertible Rescaling Network
What is IRN? Invertible Rescaling Network (IRN) is a type of network used for image rescaling. Image rescaling refers to the process of changing the size of an image while maintaining its quality. The process is complex because during downscaling, some high-frequency contents are lost, making it difficult to perfectly recover the original high-quality image. The main advantage of IRN is its ability to mitigate the ill-posedness of the process by preserving information on the high-frequency cont
LR-Net
Introduction to LR-Net LR-Net is a kind of neural network that is used for image feature extraction, which means it helps to identify patterns or important features in images. LR-Net stands for "Local Relation Network," and it is different from other types of neural networks because it uses local relation layers instead of convolutions to extract these features. In this article, we will explore what LR-Net is, how it works, and how it compares to other neural networks like ResNet. What is a N
LV-ViT
Are you familiar with LV-ViT? It's a type of vision transformer that has been gaining attention in the field of computer vision. This technology uses token labeling as a training objective, which is different from the standard training objective of ViTs. Token labeling allows for more comprehensive training by taking advantage of all the image patch tokens to compute the training loss in a dense manner. What is LV-ViT and how does it work? LV-ViT is a type of vision transformer that leverages