GitHub

ti2ni1m/Image-Generation-with-Conditional-Variational-Autoencoder-C-VAE-and-Conditional-GAN-C-GAN-

In this project, I aim to create a set of images of Kuzushiji Japanese characters via the Kuzushiji-49 dataset using 2 graphical models which are Conditional-Variational Autoencoder(C-VAE), and ...
IEEE

Latent Space Bayesian Parameter Topology Optimization of Permanent Magnet Synchronous Motors via a Variational Autoencoder

Abstract: This study introduces a novel approach that combines a variational autoencoder and Bayesian optimization to accelerate the simultaneous parameter and topology optimization of interior ...
GitHub

Variational Autoencoder for Anomaly Detection

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

Variational Auto-Encoder and Speeded-Up Robust Features Hybrid Model for Anomaly Detection and Localization in Video Sequence with Scale Variation ()

Jomo Kenyatta University of Agriculture and Technology, Juja, Kiambu County, Kenya. Where KL denotes the Kullback-Leibler divergence, and p(z) is a prior distribution over the latent space (typically ...
Visual Studio Magazine

Generating Synthetic Data Using a Variational Autoencoder with PyTorch

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 ...
IEEE

Prior Distribution Guided Gaussian Mixture Variational Autoencoder (PDGM-VAE) for Image Generation

Abstract: Variational Autoencoder(VAE) combines the ideas of autoencoders and variational inference, introducing the concept of latent space and variational inference to endow autoencoders to generate ...