GFP-GAN: An Overview
GFP-GAN is a computer program that can restore faces that have been degraded or are difficult to see. It is a type of artificial intelligence called a "generative adversarial network" or "GAN".
What is a Generative Adversarial Network?
A generative adversarial network, or GAN, is a type of artificial intelligence program that consists of two parts:
1. A generator, which creates new images or data
2. A discriminator, which evaluates whether those images or data are rea
A Ghost Bottleneck is a specific type of skip connection block used in the GhostNet CNN architecture. Similar to the basic residual block in ResNet, it integrates several convolutional layers and shortcuts. However, instead of integrating basic residual blocks, the Ghost Bottleneck stacks Ghost Modules instead.
The Ghost Module Structure
The Ghost Module structure consists of two stacked Ghost modules. The first module acts as an expansion layer, increasing the number of channels. The ratio b
A Ghost Module is a type of image block used in convolutional neural networks. Its purpose is to generate more features while using fewer parameters. To achieve this, a regular convolutional layer is split into two parts. The first part involves ordinary convolutions, but their total number is controlled. The second part involves a series of simple linear operations applied to the intrinsic feature maps generated in the first part to create more feature maps.
Why do we need Ghost Modules?
One
Overview of GhostNet
GhostNet is a type of convolutional neural network that utilizes Ghost modules, resulting in greater efficiency and increased features with fewer parameters. GhostNet is mainly made up of a stack of Ghost bottlenecks, which are grouped into different stages based on the size of their input feature maps. The final stage uses a global average pooling and a convolutional layer to transform the feature maps to a 1280-dimensional feature vector for final classification.
What a
What is GALA?
The global-and-local attention (GALA) module is a mechanism used in computer vision that enables a neural network to focus on certain regions of an image more than others. GALA stands out from other attention mechanisms because it uses explicit human supervision, which improves both the network's performance and interpretability. GALA extends a squeeze-and-excitation (SE) block with a spatial attention mechanism and uses a combination of global and local attention to determine whe
Overview of Global and Sliding Window Attention
Global and Sliding Window Attention is a pattern used in attention-based models to improve efficiency when dealing with long input sequences. It is a modification of the original Transformer model which had non-sparse attention with a self-attention component. The self-attention component had a time and memory complexity of O(n^2) which made it difficult to scale to longer input sequences. Global and Sliding Window Attention overcomes this issue b
Global Average Pooling: A Simplified Way of Feature Extraction
Global Average Pooling (GAP) is a popular operation in the field of computer vision designed to replace fully connected layers in classical Convolutional Neural Networks (CNNs). CNNs are a type of deep learning algorithm used for image recognition, classification, and segmentation tasks.
Traditionally, the final few layers of a CNN consist of a fully connected (FC) layer followed by a softmax activation function. The FC layer takes
Global Context Block is an image model block that allows modeling long-range dependencies while still having a lightweight computation. It combines the simplified non-local block and the squeeze-excitation block to create a framework for effective global context modeling.
What is Global Context Modeling?
Global Context Modeling is a technique used in computer vision to enable machines to recognize objects in images effectively. It involves considering the entire image's context, rather than j
A Global Convolutional Network, or GCN, is a type of computer algorithm used in image recognition and categorization. It is a building block used to perform two tasks simultaneously: classification and localization. The GCN uses a large kernel to generate semantic score maps, similar to the structure of a Fully Convolutional Network (FCN).
How Does a GCN Work?
A GCN employs a combination of 1xk + kx1 and kx1 + 1xk convolutions instead of directly using global convolutions or larger kernels. T
gCANS is a cutting-edge quantum algorithm used for stochastic gradient descent. This algorithm is designed to adaptively allocate shots for each gradient component measured at every iteration. With the help of advanced technology, gCANS offers an efficient way to allocate shots based on a criterion that reflects the overall shot cost for the iteration.
What Makes gCANS Unique?
The unique aspect of gCANS is that it optimizes the use of quantum resources. It does so by adaptively distributing t
The Global Local Attention Module (GLAM) is a powerful image model block that uses a cutting-edge attention mechanism to enhance image retrieval. GLAM's key feature is its ability to attend both locally and globally to an image's feature maps, allowing for a more thorough understanding of the image's content. The result is a final, weighted feature map that is better suited for image retrieval tasks.
Understanding GLAM's Attention Mechanism
GLAM's attention mechanism allows it to attend both
Understanding Global-Local Attention and Its Role in ETC Architecture
Global-Local Attention is a type of attention mechanism used in the ETC (Encoder-Transformer-Classifier) architecture that helps improve the accuracy of natural language processing tasks. It works by dividing the input data into two separate sequences - the global input and the long input - and then splitting the attention into four different components. This allows the model to selectively focus on different parts of the inp
GSoP-Net Overview: Modeling High-Order Statistics and Gathering Global Information
GSoP-Net is a deep neural network architecture that includes a Gsop block with a squeeze module and an excitation module. The GSoP block uses a second-order pooling technique to model high-order statistics and gather global information. This network architecture has been proven to be effective in various computer vision tasks, such as image classification and object detection.
The Squeeze Module
The squeeze mo
Overview of Global Sub-Sampled Attention (GSA)
Global Sub-Sampled Attention, or GSA, is a type of local attention mechanism used in the Twins-SVT architecture that summarizes key information for sub-windows and communicates with other sub-windows. This approach is designed to reduce the computational cost needed for attention mechanisms.
Local Attention Mechanisms
Before diving into GSA, it's important to understand what an attention mechanism is. An attention mechanism is a way for neural n
What are GloVe Embeddings?
GloVe Embeddings are a type of word embedding that represent words as vectors in a high-dimensional space. The vectors capture the meaning of the words by encoding the co-occurrence probability ratio between two words as vector differences.
The technique of using word embeddings has revolutionized the field of Natural Language Processing (NLP) in recent years. GloVe is one of the most popular algorithms for generating word embeddings.
How are GloVe Embeddings calcu
Glow-TTS: The Cutting-Edge TTS System That Delivers Fast, Controllable, and High-Quality Speech Synthesis
If you are looking for a state-of-the-art text-to-speech system that delivers high-quality, natural-sounding speech, look no further than Glow-TTS.
Glow-TTS is a flow-based generative model for parallel TTS that is designed to produce speech that sounds more lifelike and natural than ever before. This innovative system is able to generate speech without the need for any external alignment
GLOW is a powerful generative model that is based on an invertible $1 \times 1$ convolution. This innovative model is built on the foundational work done by NICE and RealNVP.
What is GLOW?
GLOW is a type of generative model that is used for generating complex data such as images, speech, and music. It operates by learning the underlying distribution of the data and then using this knowledge to generate samples that are similar to the original data. In other words, GLOW is used to create new d
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