What is Soft-NMS?
Soft-NMS is an algorithm that improves upon the traditional Non-Maximum Suppression (NMS) method used in object detection. NMS is used to sort detection boxes in order of their scores and eliminate those with a significant overlap with another detection box. Soft-NMS decays the scores of overlapping detection boxes gradually, allowing all objects to remain in the detection process.
Why is NMS used in Object Detection?
In object detection, the goal is to identify objects in
SoftPool: Retaining More Information for Better Classification Accuracy
What is SoftPool?
SoftPool is a new method for pooling in neural networks that sums exponentially weighted activations. This leads to a more refined downsampling process compared to other pooling methods. Downsampling is when the resolution of an activation map is reduced, making it smaller and easier to process.
Pooling is an important operation used in deep learning. It takes an input tensor (a multi-dimensional array)
Soft Split and Soft Composition: A Guide to Understanding
The FuseFormer architecture is a recently developed model that has caught the interest of the machine learning community. It has shown exceptional results in the task of image segmentation, which is used in many fields such as medical imaging, robotics, and self-driving cars. One of the unique aspects of the FuseFormer architecture is the use of Soft Split and Soft Composition operations, which we'll be discussing in this article.
What
Overview of Softmax
The Softmax function is commonly used in machine learning for multiclass classification. Its purpose is to transform a previous layer's output into a vector of probabilities. This allows us to determine the likelihood of a particular input belonging to a specific class.
How Does Softmax Work?
The Softmax function takes an input vector ($x$) and a weighting vector ($w$). It then calculates the probability that a given input belongs to a specific class (j).
Softmax works b
The Softplus function is a mathematical equation used in machine learning and neural networks as an activation function. It is used to introduce non-linearity in the output of a neuron or neural network.
What is an Activation Function?
Activation functions are used in neural networks to control the output of a neuron. A neuron is a computational unit that takes inputs, performs a calculation, and produces an output. Activation functions are applied to the output of neurons to introduce non-li
The Softsign Activation function is one of the many activation functions that researchers have developed for use in neural networks. It is sometimes used in place of the more popular activation functions, such as sigmoid and ReLU and has its own advantages and disadvantages. Below, we will take a closer look at how it works, its pros and cons, and some examples of its use in image classification applications.
How Softsign Activation Works
The Softsign activation function is defined as:
$$f\l
What is SOHO and How Does it Work?
SOHO is a computer program that learns how to recognize images and associate them with descriptive text without the need for bounding box annotations. This makes the program run ten times faster than other approaches that rely on such annotations. In SOHO, text embeddings are used to extract descriptive features from text, while a trainable CNN is used to extract visual features from the images.
SOHO learns how to extract both comprehensive and compact featur
Do you love writing songs? Are you looking for a tool to help you detect and improve the format, rhyme, and sentence integrity of your lyrics? If so, you may be interested in SongNet.
What is SongNet?
SongNet is an auto-regressive language model that is designed to help you improve the quality of your lyrics. It is built on the Transformer architecture, which has been shown to be effective at predicting sequences of text. Specifically, SongNet is tailored to the unique challenges of songwriti
SortCut Sinkhorn Attention is a type of attention model that uses a truncated input sequence in computations. This variant is an extension of Sparse Sinkhorn Attention that performs a post-sorting truncation of the input sequence. The truncation is based on a hard top-k operation on the input sequence blocks within the computational graph. Most attention models usually assign small weights and re-weight themselves during training. However, SortCut Sinkhorn Attention allows explicitly and dynamic
Understanding Source Hypothesis Transfer
Source Hypothesis Transfer, also known as SHOT, is a newly developed machine learning framework that helps to adapt models used for classification from one domain to another. This is particularly useful when you are trying to identify patterns in a dataset where data from the two domains is not the same.
The underlying idea is to freeze the classifier module (hypothesis) of the model being used in the source domain and then train a target-specific featu
Span-Based Dynamic Convolution is a cutting-edge technique used in the ConvBERT architecture to capture local dependencies between tokens. Unlike classic convolution, which relies on fixed parameters shared for all input tokens, Span-Based Dynamic Convolution uses a kernel generator to produce different kernels for different input tokens, providing higher flexibility in capturing local dependencies.
The Limitations of Classic and Dynamic Convolution
Classic convolution is limited in its abili
A sparse autoencoder is a popular type of neural network that uses sparsity as a way to compress information. The idea behind an autoencoder is to take data, like an image or a sequence of numbers, and create a compressed representation that can later be used to reconstruct the original data.
What is an Information Bottleneck?
One of the challenges with autoencoders is to find the right balance between compression and reconstruction accuracy. If we compress the data too much, it becomes hard
Sparse R-CNN: A New Object Detection Method
Object detection is a critical task in the field of computer vision, where the goal is to detect and locate objects in an image. Many object detection methods rely on generating a large number of object proposals or candidate regions, and then classifying each of these regions to determine if they contain an object. This method is known to be computationally expensive and can result in slow detection times. Sparse R-CNN is a new object detection metho
Introduction:
Attention mechanisms have become very popular in deep learning models because they can learn to focus on important parts of the input. However, the standard attention mechanism can require a lot of memory and computation, which can make it difficult to use in large-scale models. To address this issue, a new attention mechanism called Sparse Sinkhorn Attention has been proposed that is capable of learning sparse attention outputs and reducing the memory complexity of the dot-produc
Switchable Normalization (SN) is a powerful tool that can help normalize deep neural network models for improved performance. However, sometimes this technique results in over-optimization, which can lead to a phenomenon known as "overfitting". In order to address this issue, Sparse Switchable Normalization (SSN) has been developed. This technique is similar to SN but includes sparse constraints to help prevent overfitting.
What is Switchable Normalization?
In deep neural networks, normalizat
A Sparse Transformer is a new and improved version of the Transformer architecture which is used in Natural Language Processing (NLP). It is designed to reduce memory and time usage while still producing accurate results. The main idea behind the Sparse Transformer is to utilize sparse factorizations of the attention matrix. This allows for faster computation by only looking at subsets of the attention matrix as needed.
What is the Transformer Architecture?
Before diving into the intricacies
Sparsemax: A New Type of Activation Function with Sparse Probability Output
Activation functions are an essential component in deep learning models that allow for non-linear transformations between layers. One commonly used activation function is the Softmax, which is used to transform the output into normalized probabilities. However, it can often produce dense probabilities that are not computationally efficient and can emphasize the largest elements, diminishing the importance of the smaller
Overview: What is scSE?
If you've ever used an image recognition program, you know how difficult it can be to recognize objects accurately. scSE is a powerful tool that can help improve the accuracy of image recognition systems. scSE stands for spatial and channel squeeze and excitation blocks, which are modules that help encode both spatial and channel information in feature maps. In essence, the scSE block helps networks pay attention to specific regions of images, and this improves the accur