The ShuffleNet V2 Block is a component of the ShuffleNet V2 architecture which is designed to optimize speed. Speed is the main metric which is taken into consideration here instead of the usual indirect ones like FLOPs. The ShuffleNet V2 Block uses a simple operator called channel split, which takes the input of c feature channels and splits it into two branches with c - c' and c' channels, respectively. One branch remains as identity while the other branch consists of three convolutions with t
The ShuffleNet V2 Downsampling Block is an important architectural element in the ShuffleNet V2 network, which is used for spatial downsampling. By effectively removing the channel split operator, the Downsampling Block doubles the number of output channels, thereby streamlining the network's performance and speed.
What is ShuffleNet V2?
ShuffleNet V2 is a deep convolutional neural network (CNN) architecture that is specifically designed for mobile devices. It is known for its computational e
A Spatial Attention Module (SAM) is a type of module used for spatial attention in Convolutional Neural Networks (CNNs). The SAM generates a spatial attention map by utilizing the spatial relationship of different features. This type of attention is different from the channel attention, which focuses on identifying informative channels in the input.
What is Spatial Attention?
Spatial attention is a mechanism that allows CNNs to focus on the most informative parts of the input image. This is e
The Spatial Feature Transform (SFT) is a layer used in image super-resolution that generates affine transformation parameters for spatial-wise feature modulation.
What is Spatial Feature Transform?
When working with images, a common task is to convert a low-resolution (LR) image into a high-resolution (HR) image. Advanced techniques have been proposed to accomplish this task. One of these techniques is the Spatial Feature Transform (SFT), which is a neural network layer that can learn a mappi
Overview of Spatial Group-wise Enhance
Convolutional neural networks (CNNs) have taken the world by storm with their ability to recognize patterns and objects in images in a matter of seconds. However, even the best CNNs can sometimes struggle with detecting subtle differences in images or ignoring noise.
This is where a module called Spatial Group-wise Enhance comes in. It helps CNNS adjust the importance of each sub-feature by generating an attention factor for each spatial location in each
What is a Spatial Transformer?
A Spatial Transformer is a type of image model block that is used in convolutional neural networks to manipulate and transform data within the network. It allows for the active spatial transformation of feature maps, without the need for extra training supervision or optimization modifications.
Unlike pooling layers, which have fixed and local receptive fields, the Spatial Transformer module is dynamic and can actively transform an image or feature map by produci
Split attention is a technique used in machine learning to improve the performance of neural networks. It allows for attention across feature-map groups, which can be divided into several cardinal groups. This is done by introducing a new hyperparameter called the radix, which determines the number of splits within a cardinal group.
How Split Attention Works
The split attention technique involves applying a series of transformations to each individual group, resulting in an intermediate repre
Squeeze-and-Excitation Block: Boosting Network Representational Power
As technology advances, machines are becoming increasingly adept at learning from data with deep neural networks. However, even the most advanced models can fall short in representing complex features in the data. The Squeeze-and-Excitation Block (SE Block) was designed to address this issue by enabling networks to perform dynamic channel-wise feature recalibration.
At its core, the SE Block is an architectural unit that is
What is a SqueezeNeXt Block?
A SqueezeNeXt Block is a two-stage bottleneck module used in the SqueezeNeXt architecture to reduce the number of input channels to the 3 × 3 convolution. In simple terms, it is a type of computer algorithm used in image-processing tasks. It is specifically designed to reduce the number of channels in the convolution layer of the neural network, allowing for more efficient processing of images.
How does it work?
The SqueezeNeXt Block works by breaking down the in
In image processing, one of the main goals is to take a low-resolution image and make it higher quality, or in other words, make it super-resolved. This is where the SRGAN Residual Block comes in. It is a special type of block used in an image generator called the SRGAN. This generator is used specifically for image super-resolution, meaning it takes a low-quality image and produces a high-quality version of it.
What is a Residual Block?
Before we dive into the specifics of the SRGAN Residual
A Strided EESP unit is a modified version of the EESP unit, designed to learn representations more efficiently at multiple scales. This method is commonly used in neural networks for image recognition tasks.
What is an EESP Unit?
An EESP (Efficient Embedded Spatial Pyramid) unit is a type of convolutional neural network (CNN) layer used in image recognition tasks. It is designed to provide efficient and scalable representation of feature maps by using a spatial pyramid pooling (SPP) technique
What is a Style-Based Recalibration Module (SRM)?
A Style-based Recalibration Module (SRM) is a unique module that uses a convolutional neural network to recalibrate intermediate feature maps, improving the representational ability of a CNN.
By analyzing the styles in the feature maps, SRM is able to adjust its weights and either emphasize or suppress information, helping the neural network better understand the data it is processing.
How does SRM work?
The SRM model consists of two main co
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
Understanding Two-Way Dense Layer in PeleeNet
PeleeNet is a popular image model architecture that uses different building blocks to make accurate predictions. One such building block is the Two-Way Dense Layer, which is inspired by another architecture called GoogLeNet. In this article, we will understand about Two-Way Dense Layer and how it helps in getting different scales of receptive fields.
What is Two-Way Dense Layer?
Two-Way Dense Layer is a building block used in PeleeNet architectur
What is a Wide Residual Block?
A Wide Residual Block is a type of residual block that is designed to have a wider structure than other variants of residual blocks. This type of block is commonly used in convolutional neural networks (CNNs) to process images, videos or other similar data. Wide Residual Blocks were introduced in the WideResNet CNN architecture.
What is a Residual Block?
A Residual Block is a building block of a CNN that allows the network to skip over certain layers, making it
What is an XCiT Layer?
An XCiT Layer is a fundamental component of the XCiT (eX- tra large Convolutional Transformer) architecture. This architecture is an adaptation of the Transformer architecture, which is popular in natural language processing (NLP), to the field of computer vision.
The XCiT layer uses cross-covariance attention (XCA) as its primary operation. This is a type of self-attention mechanism that involves comparing different elements within a data set, rather than comparing each