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SGD with Momentum

Deep learning is a type of artificial intelligence that uses neural networks to analyze data and solve complicated problems. To train these networks, we need optimizers like stochastic gradient descent (SGD) that help us find the minimum weights and biases at which the model loss is lowest. However, SGD has some issues when it comes to non-convex cost function graphs, and this is why we use SGD with Momentum as an optimizer. Reasons why SGD does not work perfectly The three main reasons why S

SGDW

Stochastic Gradient Descent with Weight Decay (SGDW) is an optimization technique that can help in training machine learning models more efficiently. This technique decouples weight decay from the gradient update. It involves the use of several mathematical equations to help in updating the model parameters to achieve better model performance. What is Stochastic Gradient Descent? Before diving into what SGDW is, let's first discuss what stochastic gradient descent (SGD) means. SGD is an opti

Shake-Shake Regularization

Shake-Shake Regularization: Improving Multi-Branch Network Generalization Ability In the world of machine learning, deep neural networks are extensively used to solve complex problems. Convolutional neural network (CNN) is a popular type of deep neural network that is especially good at solving image classification problems. One of the CNN models that became widely known is the ResNet, which is short for residual network. ResNet is known for its deep architecture, having many layers that can ex

ShakeDrop

Overview of ShakeDrop Regularization ShakeDrop regularization is a technique that extends the Shake-Shake regularization method. This method can be applied to various neural network architectures such as ResNeXt, ResNet, WideResNet, and PyramidNet. What is ShakeDrop Regularization? ShakeDrop regularization is a process of adding noise to a neural network during training to prevent overfitting. In this method, a Bernoulli random variable is generated with probability p in each layer, which fo

Shape Adaptor

Introducing Shape Adaptor: A Revolutionary Resizing Module for Neural Networks The world of artificial intelligence and machine learning is constantly evolving, and Shape Adaptor is a prime example of how advancements in technology are shaping the future of these fields. This novel resizing module is a drop-in enhancement that can be built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution. It allows for a learnable and flexible shaping factor tha

ShapeConv

Understanding ShapeConv: A Shape-aware Convolutional Layer for Depth Feature Processing in Indoor RGB-D Semantic Segmentation ShapeConv is a type of convolutional layer that is designed for extensively processing the depth feature in indoor RGB-D semantic segmentation. This convolutional layer has been engineered for efficient and purposeful depth feature decomposition before any processing happens, making it a valuable tool for researchers and developers looking to enhance their depth feature

Shapley Additive Explanations

What is SHAP and How Does It Work? SHAP, or SHapley Additive exPlanations, is a game theoretical approach that aims to explain the output of any machine learning model. By linking optimal credit allocation with local explanations, SHAP uses classic Shapley values from game theory and their related extensions to provide explanations for machine learning models. The basic idea behind SHAP is that when a machine learning model gives a prediction, it has assigned some amount of "credit" to each fe

Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) is a powerful technique in the field of artificial intelligence and machine learning that helps to improve the accuracy and generalization of models. What is Sharpness-Aware Minimization? SAM is an optimization method that aims to minimize both the loss value and loss sharpness of a model. The traditional optimization methods only aim to reduce the loss value, which can often lead to overfitting. Overfitting is a common problem in machine learning, where a m

Shifted Rectified Linear Unit

Understand ShiLU: A Modified ReLU Activation Function with Trainable Parameters If you're familiar with machine learning or deep learning, you must have come across the term "activation function." It's one of the essential components of a neural network that defines how a single neuron behaves with its input to generate an output. One popular activation function is known as ReLU or Rectified Linear Unit. ReLU has been successful in many deep learning applications. Still, researchers have been e

Shifted Softplus

Shifted Softplus Overview Shifted Softplus is a mathematical tool used in deep learning algorithms to help create smooth potential energy surfaces. It is an activation function, denoted by ${\rm ssp}(x)$, which can be written as ${\rm ssp}(x) = \ln( 0.5 e^{x} + 0.5 )$. This function is used as non-linearity throughout the network to improve its convergence. What is an Activation Function? In the context of deep learning, an activation function is used to introduce non-linearity to the output

Short-Term Dense Concatenate

The STDC module is a tool used for semantic segmentation, which is a technique used in visual recognition tasks to identify and classify objects within an image. This module proves to be effective as it extracts deep features from images with scalable receptive fields and multi-scale information. By removing structure redundancy in the BiSeNet architecture, STDC aims to improve the efficiency of object recognition tasks. What is STDC? Short-term Dense Concatenate (STDC) is a software module d

Shrink and Fine-Tune

Understanding Shrink and Fine-Tune (SFT) If you have ever worked with machine learning or artificial intelligence, you may have heard of the term "Shrink and Fine-Tune" or SFT. SFT is an innovative approach to distilling information from a teacher model to a smaller student model. This process involves copying parameters from the teacher model and using them to fine-tune the student model without explicit distillation. In this article, we will dive more into what SFT is and how it works. What

Shuffle Transformer

Understanding Shuffle-T: A Revolutionary Approach to Multi-Head Self-Attention The Shuffle Transformer Block is a remarkable advancement in the field of multi-head self-attention. It comprises the Shuffle Multi-Head Self-Attention module (ShuffleMHSA), the Neighbor-Window Connection module (NWC), and the MLP module. This novel approach to cross-window connections is an exceptional contribution to the efficiency and performance of non-overlapping windows. Examining the Components of Shuffle Tr

ShuffleNet Block

ShuffleNet Block is a model block used in image recognition that employs a channel shuffle operation and depthwise convolutions to create an efficient architecture. The ShuffleNet Block was introduced as part of the ShuffleNet architecture, which is known for its compact design with high accuracy. What is a ShuffleNet Block? A ShuffleNet Block is a building block used in the convolutional neural networks (CNN) used for image recognition. It is designed to improve the efficiency of the archite

ShuffleNet V2 Block

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

ShuffleNet V2 Downsampling Block

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

ShuffleNet v2

Overview of ShuffleNet v2 ShuffleNet v2 is a type of neural network known as a convolutional neural network that is designed to quickly and efficiently process large amounts of data. Unlike other neural networks that focus on indirect metrics such as computing power, ShuffleNet v2 is optimized for speed. It was developed as an improvement upon the initial ShuffleNet v1 model, incorporating new features like a channel split operation and moving the channel shuffle operation lower down in the blo

ShuffleNet

ShuffleNet is a type of convolutional neural network that was developed specifically for use on mobile devices that have limited computing power. The architecture incorporates two new operations: pointwise group convolution and channel shuffle, to decrease the amount of computation necessary while still maintaining accuracy. What is a Convolutional Neural Network? Before delving into ShuffleNet, it's important to understand what a convolutional neural network (CNN) is. At its core, a CNN is a

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