PyTorch implementation of Variational Autoencoder. - AquibPy/Convolutional-Variational-Autoencoder. ... Specifically, we'll design a neural network architecture such that we impose a bottleneck in the ...

This project involves the implementation of a Convolutional Variational Autoencoder (VAE) ... model architecture, training loop, and evaluation. Model Architecture. The model uses a Convolutional ...

Generating Synthetic Data Using a Variational Autoencoder with PyTorch. Generating synthetic data is useful when you have imbalanced training data for a particular class, ... The pixel values are ...

Optimization of Graph Convolutional Networks with Variational Graph Autoencoder Architecture for 3D Face Reconstruction Task Abstract: The study addresses the fundamental challenges encountered in 3D ...

A variational autoencoder (VAE) is a deep neural system that can be used to generate synthetic data. VAEs share some architectural similarities with regular neural autoencoders (AEs) but an AE is not ...