Computer Vision

The variational autoencoder (VAE) is a type of generative model that combines principles from neural networks and probabilistic models to learn the underlying probabilistic distribution of a dataset and generate new data samples similar to the given dataset. Due to its ability to combine probabilistic modeling and learn complex data distributions, VAEs have become a fundamental tool and have had a profound impact on the fields of machine learning and deep learning....

An autoencoder is a type of artificial neural network that learns to create efficient codings, or representations, of unlabeled data, making it useful for unsupervised learning. Autoencoders can be used for tasks like reducing the number of dimensions in data, extracting important features, and removing noise. They’re also important for building semi-supervised learning models and generative models. The concept of autoencoders has inspired many advanced models. In this blog post, we’ll start with a simple introduction to autoencoders....

The VGG (Visual Geometry Group) model is a type of convolutional neural network (CNN) outlined in the paper Very Deep Convolutional Networks for Large-Scale Image Recognition. It’s known for its use of small convolution filters and deep layers, which helped it achieve top-notch performance in tasks like image classification. By stacking multiple layers with small kernel sizes, VGG can capture a wide range of features from input images. Plus, adding more rectification layers makes its decision-making process sharper and more accurate....