This project implements a Convolutional Autoencoder trained on the MNIST handwritten‐digits dataset. The goal is to learn compact latent representations of the input images and to reconstruct them ...

This project presents a comprehensive implementation of a Variational Autoencoder system designed for unsupervised anomaly detection in high-dimensional datasets. The implementation emphasizes ...

Abstract: We introduce QFARE, a hybrid quantum-classical architecture for MNIST digit classification. Our approach employs a classical variational autoencoder (VAE) to compress 28×28 grayscale images ...

Abstract: Accurate online detection or prediction of key quality variables provides critical reference information for optimizing and controlling operating variables in industrial processes. However, ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...