Generative Adversarial Networks (GANs) are a class of machine learning models based on the concept of adversarial training. They’re particularly adept at creating new and realistic data, like images, text, or even music. How GANs Work Imagine a competition between two AI models: Generator: This model acts like a creative artist, striving to produce ever-more realistic… Continue reading Generative Adversarial Networks (GANs): Where Creativity Meets Image Recognition
Tag: Machine Learning
Transfer Learning: The Knowledge-Sharing Shortcut in Machine Learning
Transfer learning in machine learning (ML) is a technique that capitalizes on knowledge gained while solving one problem to accelerate learning for a related but new problem. Why Transfer Learning? Traditional ML often trains models from scratch. This can be data-hungry and time-consuming, especially for complex tasks requiring vast amounts of labeled data. Transfer learning… Continue reading Transfer Learning: The Knowledge-Sharing Shortcut in Machine Learning
FaceX-Zoo: A PyTorch Toolbox for face recognition.
The human face is a window to identity, emotion, and even intent. Recognizing faces plays a crucial role in various applications, from security systems to social media tagging. FaceX-Zoo emerges as a powerful open-source toolbox built on PyTorch, empowering developers and researchers to tackle the complexities of face recognition. FaceX-Zoo offers a comprehensive suite of tools… Continue reading FaceX-Zoo: A PyTorch Toolbox for face recognition.
Hierarchical Clustering
Hierarchical clustering is an unsupervised machine learning algorithm used to group data points into various clusters based on their similarity. Hierarchical clustering creates a hierarchy of clusters, where each cluster can be further divided into smaller clusters. It builds a dendrogram, a tree-like structure to represent the relationships between data points. There is no need… Continue reading Hierarchical Clustering
ShuffleNet and SqueezeNet
ShuffleNet is a convolutional neural network architecture designed specifically for mobile devices with very limited computing power. ShuffleNet aims to achieve high accuracy while minimizing computation cost. ShuffleNet introduces this operation to reduce computation. It combines pointwise convolutions, 1×1 convolutions with group convolutions. The group convolutions allow efficient parallelization across channels. It groups input channels… Continue reading ShuffleNet and SqueezeNet
MobileNetV2
MobileNetV2 is a convolutional neural network architecture designed to perform well on mobile devices. It is based on an inverted residual structure, where the residual connections occur between the bottleneck layers. The intermediate expansion layer employs lightweight depth wise convolutions to filter features, serving as a source of non-linearity. It aims to strike a balance… Continue reading MobileNetV2
Support Vector Machine
Support Vector Machine (SVM) is a type of supervised learning algorithm that can be used for classification or regression tasks. The algorithm is used specifically for sample, high dimensional facial recognition problems. It is a classifier developed from a generalized portrait algorithm. In face recognition, we use the extracted face features and SVM to find… Continue reading Support Vector Machine
Haar Cascade Classifier
A Haar classifier, or a Haar cascade classifier, is a machine learning object detection program that identifies objects in an image and video. Haar Cascade classifiers are an effective way for object detection and in cases to confirm if the subject is facing straight to the camera in face recognition based applications. Haar Cascade is… Continue reading Haar Cascade Classifier
Local Binary Pattern
LBP’s simplicity, discriminative power, and computational efficiency make it a popular choice in various computer vision applications.It was first described in 1994. LBP is a type of visual descriptor that helps classify textures in images. It is a face recognition algorithm know for its performance and how it can recognize the face of a person from… Continue reading Local Binary Pattern
PyTorch: An open source ML Framework
PyTorch is an open source machine learning framework, used for applications such as computer vision and natural language processing. It is one of the preferred platforms for deep learning research. PyTorch is employed on Python, along with C/C++ libraries, and was designed for overall flexibility. PyTorch is widely used in large companies like Facebook, Twitter,… Continue reading PyTorch: An open source ML Framework