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Fundus to Angiography Generation

Fundus to Angiography Generation: A Game-Changer in Ophthalmology Fundus to Angiography Generation refers to the process of transforming a Retinal Fundus Image into a Retinal Fluorescein Angiography using Generative Adversarial Networks, or GANs. A Retinal Fundus Image displays the interior surface of the eye, including the retina, optic disc, and macula, while a Retinal Fluorescein Angiography provides information about the blood vessels within the retina. This technology has revolutionized op

Funnel Transformer

Overview of Funnel Transformer Funnel Transformer is a type of machine learning model designed to reduce the cost of computation while increasing model capacity for tasks such as pretraining. This is achieved by compressing the sequence of hidden states to a shorter one, saving the FLOPs, and re-investing them in constructing a deeper or wider model. The proposed model maintains the same overall structure as Transformer, with interleaved self-attention and feed-forward sub-modules wrapped by r

FuseFormer Block

Video inpainting is the process of filling in missing or corrupted parts of a video. This technique is used in various applications including video editing, security cameras, and medical imaging. One model used for video inpainting is the FuseFormer, which utilizes a specialized block called the FuseFormer block. What is a FuseFormer Block? A FuseFormer block is a modified version of the standard Transformer block used in natural language processing. The Transformer block consists of two part

FuseFormer

What is FuseFormer? FuseFormer is a video inpainting model that uses a feedforward network to enhance subpatch level feature fusion. It is based on specialized Transformer-based technology with novel Soft Split and Soft Composition operations. These operations divide the feature map of a video into small patches and then stitch them back together. This enhances the video's overall quality by improving the fine-grained feature fusion of the video. How Does FuseFormer Work? FuseFormer works by

G-GLN Neuron

What is a G-GLN Neuron? A G-GLN Neuron is a type of neuron used in the G-GLN architecture. The G-GLN architecture uses a weighted product of Gaussians to give further representational power to a neural network. The G-GLN neuron is the key component that enables the addition of contextual gating, allowing the selection of a weight vector from a table of weight vectors that is appropriate for a given example. How does a G-GLN Neuron work? The G-GLN neuron is parameterized by a weight matrix th

G3D

G3D is a new method for modeling spatial-temporal data that allows for direct joint analysis of space and time. Essentially, this means that it takes both spatial and temporal information into account when analyzing data, which can be useful in a variety of applications. Let's take a closer look at how it works. The Problem with Traditional Approaches to Spatial-Temporal Data In many applications, it's important to analyze data that has both spatial and temporal dimensions. For example, you m

Gait Emotion Recognition

GER, or Gait Emotion Recognition, is a novel method of recognizing human emotions based on a person's walking pattern. Researchers have developed a classifier network called STEP that uses a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture to classify an individual's perceived emotion into one of four categories: happy, sad, angry, or neutral. The STEP Network The STEP network is trained on annotated real-world gait videos, as well as synthetic gaits generated using a networ

GAN Feature Matching

GAN Feature Matching: A Method for More Efficient Generative Adversarial Network Training Introduction Generative Adversarial Networks (GANs) are a type of machine learning model that has gained popularity in recent years for their success in generating realistic images, audio, and text. However, training these models can be difficult due to the tendency to overfit, which leads to poor quality generated outputs. Feature matching is a technique that helps to address this problem by preventing t

GAN Hinge Loss

GAN Hinge Loss is a technique used in Generative Adversarial Networks (GANs) to improve their performance. GANs are a type of neural network that consists of two parts: a generator and a discriminator. The generator creates new data samples, and the discriminator determines whether a given sample is real or fake. The two parts are trained together in a loop until the generator produces samples that are indistinguishable from real data. What is Loss Function? A loss function is a mathematical

GAN Least Squares Loss

The GAN Least Squares Loss is an objective function used in generative adversarial networks (GANs) to improve the accuracy of generated data. This loss function helps GANs improve the quality of generated data by making it more similar to real data. The method used for this is called the Pearson $\chi^{2}$ divergence, which is a measure of how different two distributions are from each other. It calculates the difference between the generated distribution and the real distribution, which helps th

GAN-TTS

GAN-TTS is a type of software that uses artificial intelligence to generate realistic-sounding speech from a given text. It does this by using a generator, which produces the raw audio, and a group of discriminators, which evaluate how closely the speech matches the text that it is supposed to be speaking. How Does GAN-TTS Work? At its core, GAN-TTS is based on a type of neural network called a generative adversarial network (GAN). This architecture is composed of two main parts, the generato

Gated Attention Networks

Gated Attention Networks (GaAN): Learning on Graphs Gated Attention Networks, commonly known as GaAN, is an architectural design that allows for machine learning to occur on graphs. In traditional multi-head attention mechanism, all attention heads are consumed equally. However, GaAN utilizes a convolutional sub-network to control the importance of each attention head. This innovative design has proved useful for learning on large and spatiotemporal graphs, which are difficult to manage with tr

Gated Channel Transformation

Global Contextual Transformer (GCT) is a type of feature normalization method that is applied after each convolutional layer in a Convolutional Neural Network (CNN). This technique has been widely used in many different image recognition applications with a great level of success. GCT Methodology In typical normalization methods such as Batch Normalization, each channel is normalized independently, which can cause inconsistencies in the learned levels of node activations. GCT is different in

Gated Convolution Network

Understanding Gated Convolutional Networks Have you ever wondered how computers are able to understand human language and generate text for chatbots or voice assistants like Siri or Alexa? One sophisticated method used to achieve this is the Gated Convolutional Network, also known as GCN. It's a type of language model that combines convolutional networks with a gating mechanism to process and predict natural language. What are Convolutional Networks? Convolutional networks, also known as Con

Gated Convolution

What is Gated Convolution? Convolution is a mathematical operation that is commonly used in deep learning, especially for processing images and videos. It involves taking a small matrix, called a kernel, and sliding it over an input matrix, like an image, to produce a feature map. A Gated Convolution is a specific type of convolution that includes a gating mechanism. How Does Gated Convolution Work? The key difference between a regular convolution and a gated convolution is the use of a gati

Gated Graph Sequence Neural Networks

Gated Graph Sequence Neural Networks, or GGS-NNs, is a type of neural network that is based on graphs. It is a new and innovative model that modifies Graph Neural Networks to use gated recurrent units and modern optimization techniques. This means that GGS-NNs can take in data that has a graph-like structure and output a sequence. Understanding Graph-Based Neural Networks Before we delve deeper into GGS-NNs, it is important to have a basic understanding of Graph Neural Networks. Graph Neural

Gated Linear Network

A Gated Linear Network, also known as GLN, is a type of neural architecture that works differently from contemporary neural networks. The credit assignment mechanism in GLN is local and distributed, meaning each neuron predicts the target directly without learning feature representations. Structure of GLNs GLNs are feedforward networks comprising multiple layers of gated geometric mixing neurons. Each neuron in a particular layer produces a gated geometric mixture of predictions from the prev

Gated Linear Unit

Gated Linear Unit, or GLU, is a mathematical formula that is commonly used in natural language processing architectures. It is designed to compute the importance of features for predicting the next word. This is important for language modeling tasks because it allows the system to select information that is relevant to the task at hand. What is GLU? GLU stands for Gated Linear Unit. It is a function that takes two inputs, $a$ and $b$, and outputs their product multiplied by a sigmoidal functi

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Detection Models Mean Shift Clustering Medical Medical Image Models Medical waveform analysis Mesh-Based Simulation Models Meshing Meta-Learning Algorithms Methodology Miscellaneous Miscellaneous Components Mixture-of-Experts Model Compression Model Parallel Methods Momentum Rules Monocular Depth Estimation Models Motion Control Motion Prediction Models Multi-Modal Methods Multi-Object Tracking Models Multi-Scale Training Music Music source separation Music Transcription Natural Language Processing Natural Language Processing Courses Negative Sampling Network Shrinking Neural Architecture Search Neural Networks Neural Networks Courses Neural Search No Code AI No Code AI App Builders No Code Courses No Code Tools Non-Parametric Classification Non-Parametric Regression Normalization Numpy Courses Object Detection Models Object Detection Modules OCR Models Off-Policy TD Control Offline Reinforcement Learning Methods On-Policy TD Control One-Stage Object Detection Models Open-Domain 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Inpainting Models Video Instance Segmentation Models Video Interpolation Models Video Model Blocks Video Object Segmentation Models Video Panoptic Segmentation Models Video Recognition Models Video Super-Resolution Models Video-Text Retrieval Models Vision and Language Pre-Trained Models Vision Transformers VQA Models Webpage Object Detection Pipeline Website Monitoring Whitening Word Embeddings Working Memory Models